LADDER has 145 robot tasks. Each one is a single instruction with 1 to 10 steps (we call
them subgoals). It measures how far today's robot systems get as instructions grow longer, on two robots in three
simulators. There are three kinds of systems: a robot model (a vision-language-action policy trained on robot
data) that acts directly, an AI model that plans and gives the robot model one step at a time, and an
AI agent that controls the arm through tools.
Controlled lengthWithin a scene, a longer task keeps every subgoal of a shorter one and adds
more (138 of the 145 tasks). The effect of instruction length is measured directly.
Many kinds of instructionsEight task categories: moving objects to one or several
destinations, in a given order, through an intermediate stop, or leaving some objects in place.
Every subgoal is checkedEach subgoal is checked automatically. Results report full-task
success and the share of subgoals completed.
145tasks
3simulators
8task categories
1–10subgoals per task
10systems evaluated
2real robots
GPT-Policy · 7 subgoals4× speed
RoboLab-Long
44 tasks · Isaac Sim · Franka FR3
low-level policy: π0.5 (DROID)
GPT-Policy · 10 subgoals4× speed
SimplerEnv-Long
54 tasks · SAPIEN · WidowX 250
low-level policy: Bridge policy
GPT-Policy · 8 subgoals4× speed
RobotArena-∞-Long
47 tasks · Genesis · WidowX 250
low-level policy: Bridge policy
Eight task categories
What one instruction asks for.
Single destination 44 tasks
Every object goes to the same destination.
“put both carrots, the spoon and the eggplant on the plate”
Transfer 8 tasks
Every object starts on one destination and is moved to another.
“move the carrot and the spoon from the plate to the blue towel”
Split destination 25 tasks
Different objects go to different destinations.
“put the lemon on the plate and put the orange in the bin”
Relay 20 tasks
Objects pass through an intermediate destination.
“first put the carrot on the plate, then put the carrot on the blue towel”
Revisit 13 tasks
Objects return to a destination they have already left.
“first put the spoon on the cloth, then put the spoon in the pot, then put the spoon on the cloth”
Exclusion 13 tasks
Some objects must be left where they are.
“put everything except the eggplant on the plate”
Ordered 14 tasks
The placements must occur in a stated order.
“first put the banana in the bin, then put the orange in the bin”
Duplicates 8 tasks
Several identical objects, so the instruction refers to them by count, not by name.
“put both corn cobs on the cloth”
Results
85.4% → 0.0%
Policies fail on long instructions
Given the full instruction, the low-level policy completes 85.4% of 1-subgoal tasks, 26.9% of 2-subgoal tasks and 0.0% of 5-subgoal tasks.
26.9% → 58.4%
One subgoal at a time helps
On 2-subgoal tasks, the same policy succeeds 58.4% of the time when an orchestrator gives it one subgoal at a time and checks each one with a VLM.
43.4% → 3.4%
Orchestrators must check progress
An orchestrator that moves on without checking skips up to 43.4% of subgoals before they are done. Checking with a VLM reduces this to 3.4%.
62.0%
The best system uses a frontier VLM
GPT-Policy, an agent driven by GPT-6 Astra, completes 62.0% of all episodes, the highest of all systems.
$6.49 vs $0.33
Its success costs 20 times more
GPT-Policy spends $6.49 on API calls per successful episode. PSP-Agent, second at 40.1%, spends $0.33.
27.3%
Agents hold up on long tasks
On 5-subgoal tasks, PSP-Agent still succeeds 27.3% of the time, more than the privileged verifier with the same low-level policy (11.1%).
The longer the task, the more often robots fail
GPT-6 Astra (GPT-Policy)Gemini 3.7 Flash (PSP-Agent)Robot model alone
Share of tasks finished. Every model is on the leaderboard, under Details.
Cost
What each system spends on AI model calls.
An agent that calls a frontier model at every step is the most successful system, and by far the most
expensive. One full evaluation of GPT-Policy costs up to $8,134 in API calls.
Cost is measured from each provider's own token counts, at list prices. The robot policy runs on a
local GPU and is not included.
System
Model
Model calls per episode
Cost per episode
Cost per success
GPT-Policy
GPT-6 Astra
54
$4.02
$6.49
VoLo
Gemini 3.7 Flash
23
$0.17
$0.66
PSP-Agent
Gemini 3.7 Flash
23
$0.13
$0.33
Proprioceptive verifier
Gemini 3.7 Flash
10
$0.045
$0.18
CaP-X
Gemini 3.7 Flash
5
$0.043
$0.17
TiPToP
Gemini 3.7 Flash
1
$0.007
$0.07
42.1% / 49.5%
More time would not help much
The Proprioceptive verifier completes 42.1% of all subgoals in the first half of the time limit, and only 49.5% by the end.
36–45%
Successful episodes finish early
Successful episodes of the systems built on a low-level policy use 36–45% of the time limit on average.
Per simulator
Model calls, tokens, cost and subgoals completed, for each system.
Low-level policy
Periodic trigger
Proprioceptive trigger
Proprioceptive verifier
VoLo
CaP-X
TiPToP
PSP-Agent
GPT-Policy
Privileged verifier
VLM calls per episode
RoboLab-Long
0.0
0.0
0.0
9.3
19.2
6.3
1.0
31.8
70.8
0.0
SimplerEnv-Long
0.0
0.0
0.0
12.7
24.8
3.8
1.0
18.0
50.1
0.0
RobotArena-∞-Long
0.0
0.0
0.0
9.3
26.4
5.6
1.0
20.2
41.7
0.0
VLM calls per completed subgoal
RoboLab-Long
0.0
0.0
0.0
3.2
6.0
2.9
0.9
7.5
17.4
0.0
SimplerEnv-Long
0.0
0.0
0.0
4.9
10.1
1.6
2.8
6.1
11.8
0.0
RobotArena-∞-Long
0.0
0.0
0.0
7.3
22.2
5.1
2.1
10.7
13.3
0.0
Input tokens per episode (thousands)
RoboLab-Long
0.0
0.0
0.0
23.6
91.6
15.3
2.1
48.5
508.8
0.0
SimplerEnv-Long
0.0
0.0
0.0
17.2
70.5
7.5
2.0
27.3
357.8
0.0
RobotArena-∞-Long
0.0
0.0
0.0
12.6
75.0
12.9
2.0
31.2
293.5
0.0
Generated tokens per episode (thousands)
RoboLab-Long
0.0
0.0
0.0
2.5
7.2
5.1
0.4
16.4
8.3
0.0
SimplerEnv-Long
0.0
0.0
0.0
2.7
7.4
3.1
0.5
10.4
5.7
0.0
RobotArena-∞-Long
0.0
0.0
0.0
1.8
6.6
4.6
0.4
10.1
4.9
0.0
API cost per episode (USD)
RoboLab-Long
0.000
0.000
0.000
0.054
0.191
0.061
0.006
0.196
5.501
0.000
SimplerEnv-Long
0.000
0.000
0.000
0.046
0.161
0.034
0.007
0.119
3.861
0.000
RobotArena-∞-Long
0.000
0.000
0.000
0.032
0.162
0.054
0.006
0.123
3.177
0.000
API cost per successful episode (USD)
RoboLab-Long
0.00
0.00
0.00
0.21
0.66
0.30
0.04
0.38
8.76
0.00
SimplerEnv-Long
0.00
0.00
0.00
0.18
0.63
0.12
0.10
0.32
6.40
0.00
RobotArena-∞-Long
0.00
0.00
0.00
0.13
0.76
0.33
0.05
0.31
4.76
0.00
Subgoals completed by half of the time budget (%)
RoboLab-Long
53.1
52.6
50.2
48.4
53.6
41.6
22.3
78.4
78.1
55.9
SimplerEnv-Long
21.3
37.8
42.3
44.1
40.3
38.4
6.0
42.9
60.9
47.8
RobotArena-∞-Long
10.5
25.4
24.4
24.6
26.7
19.4
12.6
37.9
60.2
24.4
Subgoals completed by the end of the budget (%)
RoboLab-Long
56.6
57.1
58.9
56.1
62.0
42.4
22.4
81.6
78.7
63.3
SimplerEnv-Long
25.3
39.9
45.8
51.0
48.3
45.9
7.1
58.2
83.3
52.4
RobotArena-∞-Long
13.5
28.3
28.9
33.7
31.8
29.5
12.6
50.2
83.4
32.7
API cost at list prices; it covers only the hosted VLM.
Real robots
The same comparison on a real Franka and a real WidowX arm.
2 robots
WidowX 250 and Franka FR3
The low-level policy alone, the Proprioceptive verifier and PSP-Agent on tasks of 1 to 5 subgoals, 12 episodes per task and system.
Same ranking
Simulation predicts the real ranking
On the physical robots the three systems rank in the same order as in simulation.
Task category
Subgoals
Low-level policy
Prop. verifier
PSP-Agent
Franka
Single destination
5
0.0
8.3
25.0
Exclusion
2
0.0
83.3
100.0
Ordered
3
16.7
83.3
100.0
WidowX
Single destination
1
66.7
75.0
100.0
Single destination
2–3
0.0
33.3
87.5
Split destination
2–3
0.0
29.2
83.3
Exclusion
2
0.0
58.3
91.7
Ordered
2
8.3
33.3
41.7
Duplicates
2
0.0
0.0
58.3
Robot model aloneProprioceptive verifierGemini 3.7 Flash via PSP-Agent
Real-robot rollouts
The three systems on the same tasks, 12 episodes each. Each clip is typical for its system on that task: a success if the system succeeds in at least half of its episodes, otherwise a failure that completed its median number of subgoals. Speeds are relative to the control rate (5 Hz WidowX, 15 Hz Franka); the real control loop ran somewhat slower than that.
“put both the eggplant and the cup on the cloth and put the carrot in the pot”
3 subgoals
2× speed
“put everything in the pot”
3 subgoals
2× speed
“put everything except the eggplant in the pot”
2 subgoals
2× speed
“put both the eggplant and the cup on the cloth and put the carrot in the pot”
3 subgoalsfailure · 1 of 3 done
2× speed
“put everything in the pot”
3 subgoalsfailure · 1 of 3 done
2× speed
“put everything except the eggplant in the pot”
2 subgoals
2× speed
“put both the eggplant and the cup on the cloth and put the carrot in the pot”
3 subgoalsfailure · 1 of 3 done
2× speed
“put everything in the pot”
3 subgoalsfailure · 0 of 3 done
2× speed
“put everything except the eggplant in the pot”
2 subgoalsfailure · 0 of 2 done
Why tasks fail
Which subgoals fail, and whether the planner or the robot policy caused it.
7.1%
With checking, the policy causes most failures
Only 7.1% of the Proprioceptive verifier's subgoals fail because of an orchestrator decision (13.8% for VoLo). Most failed subgoals never reach the target object.
28%
Failed episodes stop making progress
Half of the failed episodes complete no further subgoal after the first 28% of the time limit.
16.2%
Agents also report success too early
PSP-Agent marks 16.2% of its subgoals as done when they are not, and TiPToP finds no plan for 50.9% of its subgoals.
Subgoals not completed at the end of the episode, by cause. Left: one bar per system. Right: all systems together, by the number of subgoals in the task. Grey and black: a planner mistake; the other colours: what happened to the object.
Which AI model plans best?
Each model answers the questions an orchestrator asks, on 145 scenes and 562 labelled images, without running the robot. Gemini 3.7 Flash, used by the orchestrators above, has the best mean score (80.8%).
Model
Planning exact match
Completion TP rate
Completion TN rate
State tracking all correct
Next subgoal valid
Tokens per query
Qwen3-VL-2B
75.2 ±7.6
68.2 ±7.0
34.1 ±5.9
9.3 ±4.0
58.9 ±5.3
–
Qwen3-VL-8B
91.0 ±5.8
69.0 ±6.8
79.9 ±5.7
47.9 ±5.5
72.3 ±4.4
–
Qwen3.5-9B
86.9 ±6.5
44.4 ±6.3
96.9 ±2.5
11.7 ±3.7
63.2 ±4.8
5,173
Qwen3.8-27B
91.0 ±5.8
76.2 ±6.3
84.8 ±4.3
58.2 ±4.8
79.3 ±4.1
1,904
Gemini 3.1 Pro
89.7 ±6.0
54.8 ±6.6
96.0 ±2.9
68.7 ±4.3
75.2 ±4.5
583
Gemini 3.7 Flash
93.1 ±5.4
59.4 ±6.6
96.6 ±2.6
70.8 ±4.0
81.4 ±4.2
412
Gemini Robotics-ER 2
90.3 ±5.9
54.0 ±6.5
97.8 ±2.3
69.6 ±4.2
76.6 ±4.1
510
GPT-5.6 Sol
93.1 ±5.4
72.8 ±6.3
86.4 ±4.3
56.4 ±4.7
82.0 ±4.3
282
GPT-6 Astra
91.7 ±5.7
71.1 ±6.2
86.4 ±4.4
53.2 ±5.1
85.4 ±4.0
203
Claude Opus 5
90.3 ±5.9
68.6 ±6.6
87.9 ±4.1
61.0 ±4.7
79.8 ±4.4
400
Scores in % with 95% intervals. Planning: the list of subgoals is exactly right. Completion: finished (TP) and unfinished (TN) subgoals judged correctly. State tracking: the status of every subgoal is right. Next subgoal: the proposed next step is valid.
Videos
Recorded attempts from the evaluation. The label on each video gives its playback speed.
Highlights: long tasks solved without a single mistake
2× speed
CaP-X
“first put the orange in the bin, then put the pumpkin in the bin, then put the lychee in the bin, then put the lemon in the bin, then put the lime in the bin, then put the banana in the bin, then put the onion in the bin, then put the avocado in the bin”
8 subgoals
2× speed
GPT-Policy
“first put the lemon on the plate, then put the lemon in the bin, then put the lime on the plate, then put the lime in the bin, then put the banana on the plate, then put the banana in the bin”
6 subgoals
2× speed
GPT-Policy
“put the avocado, the banana and the lemon on the plate and put the orange, the lime and the onion in the bin”
6 subgoals
One scene, growing horizon
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoalsfailure · 9 of 10 done
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoalsfailure · 0 of 6 done
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoalsfailure · 0 of 10 done
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
2× speed
“put the banana in the bin”
1 subgoal
2× speed
“put the banana, the orange, the lemon, the lime, the avocado and the onion in the bin”
6 subgoals
2× speed
“put the banana, both oranges, the lemon, the lime, the avocado, the onion, the lychee, the pumpkin and the pomegranate in the bin”
10 subgoals
Task categories
2× speed
Proprioceptive trigger
“move the orange, the lemon and the lime from the plate to the bin”
3 subgoalsTransfer
2× speed
Privileged verifier
“put everything except the red block and the blue block in the bin”
Highlights: long tasks solved without a single mistake
2× speed
CaP-X
“first put the carrot on the plate, then put the spoon on the plate, then put the eggplant on the plate, then put the carrot on the blue towel, then put the spoon on the blue towel, then put the eggplant on the blue towel, then put the carrot on the plate, then put the spoon on the plate, then put the eggplant on the plate, then put the carrot on the blue towel”
10 subgoals
2× speed
PSP-Agent
“first put the carrot on the plate, then put the spoon on the plate, then put the eggplant on the plate, then put the carrot on the blue towel, then put the spoon on the blue towel, then put the eggplant on the blue towel, then put the carrot on the plate”
7 subgoals
2× speed
VoLo
“first put the carrot on the plate, then put the carrot on the blue towel, then put the spoon on the plate, then put the spoon on the blue towel, then put the eggplant on the plate, then put the eggplant on the blue towel”
6 subgoals
One scene, growing horizon
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoals
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoalsfailure · 4 of 5 done
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 5 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoalsfailure · 4 of 5 done
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 6 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 8 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 9 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 8 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 7 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoalsfailure · 2 of 5 done
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoalsfailure · 0 of 10 done
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoals
2× speed
“put the carrot on the plate”
1 subgoal
2× speed
“put both carrots, both spoons and the eggplant on the plate”
5 subgoals
2× speed
“put the four carrots, the three spoons and the three eggplants on the plate”
10 subgoals
Task categories
2× speed
Proprioceptive verifier
“put both carrots, both spoons and the eggplant on the plate”
5 subgoalsSingle destination
2× speed
Proprioceptive verifier
“first put the carrot on the plate, then put the carrot on the blue towel, then put the spoon on the plate, then put the spoon on the blue towel, then put the eggplant on the plate”
5 subgoalsRelay
2× speed
GPT-Policy
“first put the carrot on the plate, then put the spoon on the plate, then put the eggplant on the plate”
Highlights: long tasks solved without a single mistake
2× speed
GPT-Policy
“put the blue bowl, the sushi toy, the cheese, the banana and the mushroom on the purple cloth”
5 subgoals
2× speed
PSP-Agent
“put the blue bowl, the sushi toy, the banana and the cheese on the purple cloth”
4 subgoals
2× speed
VoLo
“put the blue bowl, the sushi toy, the cheese and the banana on the purple cloth”
4 subgoals
One scene, growing horizon
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoals
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoals
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoalsfailure · 1 of 3 done
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 1 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoalsfailure · 2 of 3 done
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 3 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoalsfailure · 2 of 3 done
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 4 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoals
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 4 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoals
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 3 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoalsfailure · 1 of 3 done
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 2 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoalfailure · 0 of 1 done
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoalsfailure · 1 of 3 done
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 0 of 6 done
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoals
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoals
2× speed
“put the blue bowl on the purple cloth”
1 subgoal
2× speed
“put the blue bowl, the sushi toy and the banana on the purple cloth”
3 subgoals
2× speed
“put the blue bowl, the sushi toy, the cheese, the banana, the mushroom and the corn cob on the purple cloth”
6 subgoalsfailure · 4 of 6 done
Task categories
2× speed
PSP-Agent
“put the spoon and the corn cob on the cloth and put the chicken drumstick in the pot”
3 subgoalsSplit destination
2× speed
GPT-Policy
“first put the spoon on the cloth, then put the spoon in the pot, then put the spoon on the cloth”
3 subgoalsRevisit
2× speed
GPT-Policy
“put the three corn cobs on the cloth”
3 subgoalsDuplicates
One task, every system
RoboLab-Long, 4 subgoals: “put the avocado and the banana on the plate and put the orange and the lime in the bin”. Episodes are sampled at random, so failures are shown where they occurred.
4× speed
Low-level policy
failure
4× speed
Periodic trigger
success
4× speed
Proprioceptive trigger
failure
4× speed
Proprioceptive verifier
failure
4× speed
VoLo
success
4× speed
CaP-X
success
4× speed
TiPToP
success
4× speed
PSP-Agent
success
4× speed
GPT-Policy
success
4× speed
Privileged verifier
success
The same policy, with and without an orchestrator
Given the whole instruction at once, the low-level policy stalls after the first subgoals. Driven by the Proprioceptive verifier, it completes the task.
Relay · 6 subgoals: “first put the lemon on the plate, then put the lemon in the bin, then put the lime on the plate, then put the lime in the bin, then put the banana on the plate, then put the banana in the bin”
4× speed
Low-level policy failure
4× speed
Proprioceptive verifier success
Ordered · 4 subgoals: “first put the orange in the bin, then put the lemon in the bin, then put the lime in the bin, then put the banana in the bin”
4× speed
Low-level policy failure
4× speed
Proprioceptive verifier success
Evaluate your system
Any policy, orchestrator or agent can join the leaderboard.
@article{ladder2026,
title = {{LADDER}: A Long-Horizon Robot Manipulation Benchmark},
author = {Petkov, Petko and Albanese, Giuliano and Nikolov, Nikolay and Van Gool, Luc and Tremblay, Jonathan and Paudel, Danda Pani},
journal = {arXiv preprint arXiv:2610.18053},
year = {2026},
url = {https://arxiv.org/abs/2610.18053}
}
GPT-Policy · 7 subgoals
4× speed
GPT-Policy · 10 subgoals
4× speed
GPT-Policy · 8 subgoals
4× speed
