EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies
Authors: Chi Kit Ng, Yidong Zhang, Tianchun Wu, Siu Hing Lui, Hanlin Zhao, Huxin Gao, Hongliang Ren
Organizations: Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to interpret experimental failures and decide how controllers should be revised. We present EndoRSI (Endoscopic Recursive Self-Improvement), a human-governed, self-improving robotic research system in which the embedded EndoForge coding agent iteratively develops and refines endoscopic robot policies using evidence from physical experiments. Physical execution remains isolated within a safety-constrained runtime under human authorisation. In motorised endoscopy, validation evidence first caused the agent to reject an initial action representation, while subsequent physical experiments falsified an offline-supported insertion strategy by revealing hazardous closed-loop behaviour absent from the source data. Failed physical rollouts were then incorporated into policy revision; insertion success subsequently increased from 2/21 in the first generation to 17/21 in the fourth. The frozen final policy retained competence across visual and hardware shifts and was the best performing autonomous policy across six independent ex-vivo porcine specimens. These results show that physical robot interaction can serve as experimental evidence for agent-driven robot policy research.
Figures & tables
Figure 1: EndoForge connects task definition, policy research, physical execution and evidence-driven revision. Existing tracking and navigation data provide complementary source skills. The coding agent analyses data, results and code and prepares candidate policies. Human-authorised execution occurs through a separate safety-constrained runtime. Physical outcomes and human feedback inform subsequent research and transfer evaluation.
Figure 2: Physical evidence changes subsequent research decisions. a , The research loop connects analysis, hypotheses, candidate implementation, authorised experiments, outcome feedback and revision. b , The coding agent acts offline; a separate runtime executes the fixed policy under human supervision. c , Division of responsibilities among the coding agent, human investigators and runtime. d , The experimental progression from source-skill validation to physical falsification, failure-derived supervision and transfer. Success, failure and emergency-stop outcomes are denoted S, F and E, respectively.
Figure 3: Validation evidence redirects policy design. a , Source tracking and navigation tasks, coding-agent activities and candidate dispositions. b , Held-out image-conditioned reference comparisons. Diamond markers indicate the reported MAE values at the bar endpoints; thin horizontal lines lead to the numerical labels and do not indicate uncertainty intervals. c , Validation MAE during the progression from v1 to direct continuous prediction in version 2 (v2) and temporal ensembling (TE). Validation determined candidate selection; held-out evaluation quantified generalisation. The validation and held-out comparisons use different episodes and therefore different baseline values. Exact split-specific values and metrics are reported in Supplementary Note 3 and Supplementary Tables 7 and 8. MAE is expressed in motor units. ZS-B denotes the dual-policy router and ZS-C the instructed VLA policy.
Figure 4: Physical experimentation falsifies an offline-supported insertion strategy. a , Three pre-defined directions and their dispositions: ZS-A servo gate, dual-policy router (ZS-B) and instructed VLA policy (ZS-C). b , First-lunge times for all 16 ZS-A attempts, a representative forward-command and bending trace, and representative frames illustrating premature advancement without a reactive stop. All 16 attempts failed and required emergency stopping. The median first-lunge time was 0.15 s; the gate required four consecutive low-bending predictions.
Figure 5: Physical evidence supports continued policy improvement. a , Development of the instructed VLA direction into a reactive policy. b , Source-derived supervision used to initialise Gen1; the mixture contains 70% skill retention, 20% composed insertion and 10% stop proxies. c , Physical outcomes and research revisions across Gen1–Gen4, including changes to instructions and execution settings. Gen1-to-Gen2 also included success-imitation action-head fine-tuning. d , Successful actions supply cloning targets; unsuccessful states supply no-forward targets with steering retained. e , Success rates across 21 attempts per generation; the line connects the observed proportions, and each dot denotes one trial outcome (S, F or E). Wilson 95% CIs and exact outcomes are reported in Table 1 ; exact comparisons are in Supplementary Table 12. Generation comparisons include checkpoint, instruction and execution-profile changes and do not isolate their individual effects.
Policy arm
Success / n
Success, % (95% CI)
Emergency stop
ZS-A rule composition
0/16
0.0 (0.0–19.4)
16
CNN behaviour cloning
0/21
0.0 (0.0–15.5)
0
Action-chunking transformer
1/21
4.8 (0.8–22.7)
1
Diffusion Policy
2/21
9.5 (2.7–28.9)
0
IBVS
3/21
14.3 (5.0–34.6)
0
Success-only, final iteration
3/21
14.3 (5.0–34.6)
0
Table 1: Physical insertion outcomes in the benchtop development environment. All arms contain 21 attempts except ZS-A, which stopped after 16 consecutive emergency stops. Success proportions use Wilson 95% CIs; ordinary failures and emergency stops remain unsuccessful in every denominator. Exact two-sided Fisher comparisons are reported in Supplementary Table 12.
Figure 6: The evolved policy transfers across deployment shifts and biological tissue. a , Gen4 outcomes across deployment conditions (21 attempts per condition). b , Autonomous outcomes in two ex-vivo cohorts (three specimens per cohort; seven attempts per condition and specimen). Dots show individual outcomes; intervals show Wilson 95% confidence intervals. c , Representative starting, entrance-alignment and lumen or terminal views from successful Gen4, successful baseline and failed trials. Failure sequences do not establish lumen centring. These examples add no trials or biological replicates.
Supplementary Figure 1: Commercial endoscopes and motorised robot configurations. The upper row shows the medical gastroscope, its motorised knob adapter and the assembled ER1 platform (Olympus GIF-Q260J; 9.9-mm insertion diameter). The lower row shows industrial endoscopes and adapter designs used for ER2 (Ailishi S50; 6.0 mm) and ER3 (Ailishi S50; 3.9 mm). Labels (1) and (2) identify the illustrated industrial steering adapters; (3) identifies the shared forward actuator. ER1 supplied tracking data, ER2 supplied navigation data and supported policy development and ex-vivo experiments, and ER3 was used for different-instrument transfer. The original hardware illustration is reproduced without modification. Dimensions are those labelled in the supplied illustration; the arrangement is not a calibrated scale comparison.
Supplementary Figure 2: Ex-vivo evaluation and a successful Gen4 trial. a , ER2 evaluation workflow. Here, zero-shot denotes transfer of the fixed policy to ex-vivo tissue without adaptation. b , Six independent porcine tracheae: specimens 1–3 from the initial block cohort and specimens 4–6 from the interleaved replication cohort; no calibrated scale is implied. c , One successful Gen4 trial on specimen 4 from the interleaved replication cohort. The first two rows sample the first 6.00 s; the next two sample the full recording through 22.80 s. These are two temporal samplings of the same trial, with repeated frames and a reset time axis. Labels give zero-based frame indices and elapsed video times at 20 frames s -1 , not scored traversal times. This qualitative example adds no trials or biological replicates.
Supplementary Figure 3: Representative unsuccessful ex-vivo trials. a , Gen4 on specimen 6 from the interleaved replication cohort. b , Gen1 on specimen 2 from the initial block cohort. In each panel, the first two rows sample the first 6.00 s, and the next two sample the full recording, through 95.15 s in a and 8.05 s in b . Each panel shows one trial with two temporal samplings, repeated frames and a reset time axis. Labels give zero-based frame indices and elapsed video times at 20 frames s -1 . Both trials have unsuccessful canonical outcomes; the terminal views do not establish successful lumen alignment. The images alone do not establish failure mechanism or emergency-stop activation. These qualitative examples add no trials or biological replicates.
Stage
Scientific role
Evidence and decision
Frozen output
1
Audit source skills
Verify image–action correspondence, episode splits, ranges and task statistics
Locked splits, normalisation and audit report
2
Test policy hypotheses
Reject v1 on validation; replace discrete residual actions with continuous chunks and temporal ensembling (TE)
Selected source-policy checkpoint and validation report
3
Operationalise a task hypothesis
Human specifies align-then-advance; agent quantifies source-task support, rejects unsupported navigation-centroid steering and implements three candidates
Cross-task analysis; zero-shot composition A (ZS-A), dual-policy router and vision–language–action (VLA) manifests
Define the task and constraints; reset and authorise each trial
Retain deployment and emergency-stop authority; assign terminal S/F/E outcomes
Coding agent
Audit data and results; implement and revise candidates between trials
Prepare a future checkpoint, manifest, instruction and execution limits for human authorisation
Frozen candidate
One authorised checkpoint, manifest, instruction and limit configuration
Remains fixed throughout the armed trial
Visual processing
Full endoscopic frames pass through preprocessing and asynchronous inference
Semantic action predictions enter an action queue
Execution runtime
The 20-Hz loop selects valid queued actions
Deterministic magnitude, watchdog and slew limits bound the command
Platform adapter
Convert semantic commands to the selected platform’s motor mapping
Send platform-specific packets to serial firmware
Supplementary Table 2: Control flow and safety authority. The coding agent acts between trials and has no camera or motor interface while the robot is armed. S, F and E denote success, ordinary failure and emergency stop.
Platform
Instrument
Dataset or experiment role
Lower controller
ER1
Olympus GIF-Q260J medical gastroscope, 9.9 mm
Tracking source dataset in discarded ex-vivo pig stomach
Four-motor firmware
ER2
Ailishi S50 industrial endoscope, 6.0 mm
Navigation source dataset; development and evolution aperture; low illumination; phantom-configuration shift; both ex-vivo cohorts
Six-channel firmware (four motors are used); M1–M4 active, M5/M6 zero
ER3
Ailishi S50 industrial endoscope, 3.9 mm
Different-instrument transfer arm
Same six-channel firmware (four motors are used) and active-channel pattern as ER2
Supplementary Table 3: Instrument, experiment and controller mapping.
Layer
Input and output
Reproducible role in the archive
Platform collection
Camera, timestamp and executed device command → recorded episode
Three device-facing collection paths preserve the raw embodiment and acquisition context
Dataset conversion
Recorded episode → aligned image–semantic-action records
Validates image/action correspondence, constructs episode-disjoint splits and converts commands to [m1,m3,m4]
Training
Canonical records and task normalisation → frozen checkpoint and manifest
Learns the action controller without embedding a raw ER1/ER2/ER3 serial packet
Inference runtime
Current image, instruction and frozen manifest → bounded semantic action
Runs asynchronous model inference and a deterministic 20-Hz watchdog/safety boundary
Hardware adapter
Semantic action plus platform profile → device motor packet
Applies channel expansion, sign, scale, gain, bias and inactive-channel rules
Lower firmware
Device motor packet → motor actuation
Separate four-motor medical and six-channel industrial Keil projects
Supplementary Table 4: Relationship among collection data, learned policy and hardware code.
Task
Episodes
Action rows
Split
Test n
Collection system and environment
Tracking
100
12,710
80/10/10
10 episodes
ER1, Olympus GIF-Q260J; ex-vivo pig stomach
Navigation
100
16,534
80/10/10
10 episodes
ER2, Ailishi S50 6.0 mm; colon phantom
Supplementary Table 5: Source-dataset audit and experimental units.
Tracking
Navigation
Policy
Validation
Held-out
Validation
Held-out
CNN behaviour cloning
2.619
2.316
19.463
23.580
Agent v1
2.654
2.065
19.811
23.608
Agent v2
2.615
2.255
19.091
22.032
Agent v2+TE
2.426
2.112
18.791
21.683
Supplementary Table 6: Validation-based model selection and post-selection held-out performance. Values are frame-pooled mean absolute error (MAE) in raw motor units; lower is better. Each task contains 100 episodes split into 80 training, 10 validation and 10 test episodes. Validation determined candidate selection; held-out scores were evaluated afterwards. TE denotes causal temporal ensembling of the selected v2 checkpoint and is a decoding procedure, not a separately trained model.
Task
Policy
MAE
RMSE
Sign accuracy
Tracking
Classical visual servoing
2.846
4.006
0.778
Diffusion Policy
2.340
3.891
0.780
Action-chunking transformer
2.417
3.830
0.781
CNN behaviour cloning
2.316
3.697
0.817
Image-only VLA
2.622
4.223
0.611
v1
2.065
3.884
0.741
Supplementary Table 7: Complete held-out image-conditioned offline results. Lower MAE and RMSE are better; higher sign accuracy is better. The copy-previous diagnostic is excluded because it is teacher forced.
Task
Original
Seed 1
Seed 2
Mean ± s.d.
CNN reference
Tracking
2.112
2.076
1.995
2.061±0.060
2.316
Navigation
21.683
21.257
21.615
21.518±0.229
23.580
Supplementary Table 8: Selected-policy stability across three training runs. Values are held-out test MAE; s.d. denotes standard deviation.
Claim or candidate
Sample size
Evidence
Decision
Tracking centre error predicts bending
10,284 frames
R2=0.340 ( m3 ), 0.370 ( m4 )
Retain as alignment primitive
Navigation dark-centroid error predicts bending
13,810 frames
R2=−0.034 ( m3 ), 0.004 ( m4 )
Reject
Low bending predicts advancement
13,810 frames
Mean m1=16.746 in lowest quartile, ≤0.560 otherwise
Retain as conditional advance principle
ZS-A composes both rules safely
16 physical attempts
0 success; 16 emergency stops
Physical falsification rejects rule composition
Supplementary Table 9: Source-task evidence and its disposition.
Each update used successes from its own preceding batch and discarded unsuccessful outcomes
3/21 final
Supplementary Table 10: Generation-specific development evidence and update recipes. Source-batch outcomes are recorded in the supplied per-trial outcome correspondence; the last column gives the final video-reviewed performance of the resulting policy. Comparisons between lineages are descriptive because their evidence pools and starting checkpoints differed.
Group
Arm
Success / n
Success, % (95% CI)
Emergency stop
Physical falsification
ZS-A
0/16
0.0 (0.0–19.4)
16
Fixed reference
CNN behaviour cloning
0/21
0.0 (0.0–15.5)
0
Action-chunking transformer
1/21
4.8 (0.8–22.7)
1
Diffusion Policy
2/21
9.5 (2.7–28.9)
0
IBVS
3/21
14.3 (5.0–34.6)
0
Success-only
Iteration 1
1/21
4.8 (0.8–22.7)
0
Supplementary Table 11: Complete benchtop outcomes with exact sample sizes and Wilson 95% confidence intervals (CIs). Emergency-stop counts come from benchtop operator 1’s live record; endoscopic video alone does not establish emergency-stop activation.
Comparison
Odds ratio
P
Role
Gen4 versus Gen1
40.38
5.75×10−6
Overall generation contrast
Gen4 versus IBVS
25.50
3.21×10−5
Best fixed classical reference
Gen4 versus final Success-only iteration
25.50
3.21×10−5
Failure-evidence mechanism control
Gen2 versus Gen1
5.85
0.0670
Generation comparison
Gen3 versus Gen2
3.25
0.121
Generation comparison
Gen4 versus Gen3
2.13
0.484
Generation comparison
Supplementary Table 12: Two-sided Fisher exact tests for final phantom contrasts and descriptive generation comparisons. Generation comparisons quantify observed proportions; causal interpretation is limited because the Success-only and generation lineages used different evidence pools and starting checkpoints.
Human investigators
Coding agent
Define research goal, align-then-advance task and bounded deployment authority.
Plan dataset audit, validation-based revision and physical evaluation.
Provide existing tracking and navigation datasets and experimental constraints.
Audit image-action records, lock episode splits and implement comparison policies.
Require validation evidence to guide policy improvement.
Reject v1 on validation. Implement continuous action chunks and causal temporal ensembling.
Specify insertion as tracking plus navigation. Require freezing before insertion outcomes.
Authorise ZS-A tests and use emergency stop in all 16 attempts.
Analyse discontinuous advancement and absence of a reactive stop response.
Prioritise end-to-end VLA development and specify stop-on-contact behaviour.
Develop the previously frozen VLA direction into Gen1 using source-derived supervision.
Supplementary Table 13: Human–agent division of scientific decisions. Rows follow the recorded order of research decisions. Investigators retain deployment and emergency-stop authority. ZS-B denotes the dual-policy router.
Policy
Specimen
n
S
F
E
Success navigation time, s
IBVS
1
7
3
3
1
6.2 [4.9–14.6]
2
7
3
3
1
2.0 [1.9–2.1]
3
7
3
4
0
2.5 [1.9–3.2]
Gen1
1
7
2
5
0
9.3 [8.9–9.6]
2
7
4
3
0
3.5 [2.4–4.5]
3
7
3
3
1
6.7 [4.7–6.7]
Supplementary Table 14: Initial-cohort ex-vivo outcomes and successful navigation times by policy and specimen. The specimen is the biological unit; attempts are repeated technical measurements. F and E are distinct live-operator events but both are binary failures. Navigation time is conditional on success and shown as median [IQR].
Condition
Success / n
Success, % (95% CI)
Exploratory P vs Gen4
Scope
Initial block cohort
Gen4
21/21
100.0 (84.5–100.0)
–
Frozen evolved policy
Success-only
11/21
52.4 (32.4–71.7)
4.79×10−4
Final success-only control
Gen1
9/21
42.9 (24.5–63.5)
5.32×10−5
Initial reactive policy
IBVS
9/21
42.9 (24.5–63.5)
5.32×10−5
Classical reference
Interleaved replication cohort
Supplementary Table 15: Cohort-specific pooled descriptive outcomes and exploratory attempt-level comparisons. Each cohort contains three independent specimens and seven technical repetitions per condition–specimen cell. Cohorts are analysed separately. Wilson 95% CIs are descriptive; two-sided Fisher tests compare autonomous controls with Gen4 within the same cohort and do not provide specimen-level or causal inference. The initial Gen4 row includes its 21 protocol attempts only. Teleoperation rows are single-operator references and were not tested for an experience effect.
Arm
Outcome
n
Mean, s
Median [IQR], s
Range, s
Emergency stop
Success-only 1
F
20
4.6
4.1 [3.6–5.0]
3.3–10.2
0
Success-only 2
F
18
3.5
3.2 [3.1–3.7]
2.5–7.0
0
Success-only 3
F
18
3.8
3.5 [3.2–4.2]
3.0–5.6
0
Success-only 4
F
18
4.8
4.2 [3.5–4.6]
2.7–13.9
0
Gen1
F+E
19
12.4
5.3 [3.8–6.9]
1.9–97.8
5
Gen2
F
13
9.2
8.0 [6.9–9.9]
3.4–21.2
0
Supplementary Table 16: Descriptive video-duration phenotypes for Success-only and generation arms. Duration includes the recorded trial video and is influenced by operator termination; it is not a time-to-event efficacy analysis. Median values are shown with IQR. Emergency-stop counts come only from benchtop operator 1’s live record.
Domain
Comparison
Exact-path n
Agreement, %
Reliability
All scored trials
Relevant live record vs external evaluator 1
546
100.0
Cohen’s κ=1.00
All scored trials
Three rating sources per trial
546
100.0
Fleiss’ κ=1.00
Phantom
Benchtop operator 1 vs video evaluator 1
315
100.0
Cohen’s κ=1.00
Phantom
Benchtop operator 1 vs external evaluator 1
315
100.0
Cohen’s κ=1.00
Phantom
All three rating sources
315
100.0
Fleiss’ κ=1.00
Initial ex vivo
Initial ex-vivo operator 1 vs video evaluator 2
84
100.0
Cohen’s κ=1.00
Supplementary Table 17: Binary outcome agreement after analysing every E outcome as F. The scored set comprises 315 phantom trials, 84 initial-cohort ex-vivo trials and 147 replication-cohort trials. The three-way analysis uses three rating sources per trial: live record, video evaluator 1 and external evaluator 1 for benchtop trials; live record, video evaluator 2 and external evaluator 1 for the initial cohort; and the three video evaluators for replication. Emergency-stop counts are not inferred from video and therefore come only from the relevant live record. Exact-path matching is used, with complete binary outcome ratings from the three selected sources for all scored trials.
Cohort
Analysis
n
Estimate
95% CI
Interpretation
Initial block
ICC(A,1)
48
0.994
0.978–0.998
Individual-rating agreement
Initial block
ICC(A, k )
48
0.998
0.993–0.999
Three-rating mean reliability
Interleaved replication
ICC(A,1)
76
0.956
0.873–0.984
Individual-rating agreement
Interleaved replication
ICC(A, k )
76
0.985
0.954–0.995
Three-rating mean reliability
Supplementary Table 18: Reliability of successful ex-vivo navigation-time annotations. The intraclass correlation coefficient (ICC) is reported as ICC(A,1) for two-way random-effects absolute agreement of a single rating and as ICC(A, k ) for reliability of the three-rating mean. ICC 95% CIs use 10,000 trial-level bootstrap resamples with a fixed random seed for each cohort.
Initial block cohort
Protocol condition
Specimen 1
Specimen 2
Specimen 3
Total
IBVS
3/7
3/7
3/7
9/21
Gen1
2/7
4/7
3/7
9/21
Success-only, original configuration
3/7
4/7
4/7
11/21
Gen4
7/7
7/7
7/7
21/21
Interleaved replication cohort
Supplementary Table 19: Transfer outcomes across the two ex-vivo cohorts and six porcine tracheae. Specimens are independent biological units; each specimen cell contains seven technical repetitions, and Total aggregates the three cells within a cohort. Each cohort block lists only conditions evaluated in that protocol. Within replication, the two Success-only rows use the same checkpoint and differ only in the runtime execution profile: action scaling, gains and smoothing (Supplementary Note 5).
Adapting robot manipulation policies to new tasks and environments remains highly data-intensive, while the data needed for further improvement depends on the policy's current capabilities and failure modes. We introduce EmbodiRSI, an agentic system for recursive self-improvement (RSI) in a real-to-sim-to-real setting, where task-specific simulations are constructed from target deployment scenarios and used as low-cost environments for iterative policy improvement before transfer back to the physical world. EmbodiRSI uses policy execution feedback to guide subsequent experience acquisition and policy updates. Two complementary mechanisms close this loop: Collaborative Error Correction generates agent-assisted corrective trajectories from policy-reached states, while Adaptive Data Collection directs expert demonstration generation toward the current policy's weaknesses. The task-specific simulation serves as a reusable workspace for policy warm-up, repeatable evaluation, failure diagnosis, and targeted data generation across successive RSI rounds. Across three tabletop environments and 14 subtasks, EmbodiRSI increases scene-balanced autonomous simulation success from 50.4% to 83.5% over two RSI updates. With 400 adaptive simulated trajectories and only ten real-world refinement trajectories per subtask, EmbodiRSI achieves 83.1% scene-balanced autonomous real-world success, compared with 75.0% for adaptation using 200 real-world demonstrations per subtask. These results demonstrate that feedback-driven recursive improvement in deployment-specific simulations can enable data-efficient adaptation of embodied policies to physical environments.
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the scene, execute a policy, verify the outcome, and refine the next iteration. To bridge this gap, we introduce ENPIRE, a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with one or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes. This closed-loop system transforms real-world manipulation learning into a controllable optimization procedure, minimizing human effort while allowing fair ablations across training recipe and agent variants. Powered by ENPIRE, frontier coding agents can autonomously train a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks, such as organizing a pin box, fastening a zip tie, and tool use, a process that further accelerates when we dispatch an agent team on a robot fleet. Our results suggest a practical and scalable path toward deploying coding agents to autonomously advancing robotics in the physical world.
Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model. A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections. The meta-agent can also improve its own diagnostic tools, so retained revisions support both later action and later self-improvement. This process develops joint-level control, evidence-seeking observation, and reusable manipulation skills without model-weight updates or a separately trained action policy. Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions yields a five-dimension average score of 68.14/100 and 62.00% success, compared with 47.17% for RoboDawn's one-shot Astra agent, the strongest published reference in our comparison. On eight manipulation tasks challenging direct Astra, PhysEvo achieves 55.00% success, compared with 1.25% for the direct-Astra reference. Deploying the simulation-evolved harness on AgileX PiPER and continuing skill revision yields 90.60/100 average score and 84.00% success across 25 trials on five real-world tasks. PhysEvo turns the consequences of action into persistent, testable changes to how a frozen model acts and improves.
Wenqing Tian, Zeyu Zhang, Zhaocheng Liu +3
University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences · Independent Researcher +1