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DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation
DexHiL proposes a human-in-the-loop post-training framework for dexterous manipulation vision-language-action models, integrating offline demonstrations, online human takeover, and intervention-aware reweighted training…
Summary
DexHiL proposes a human-in-the-loop post-training framework for dexterous manipulation vision-language-action models, integrating offline demonstrations, online human takeover, and intervention-aware reweighted training into a single arm-hand system. Its goal is to improve real-robot success rates and robustness on high-dimensional, contact-rich dexterous-hand tasks more efficiently than pure offline finetuning.
Problem
- Existing VLAs show promise in general manipulation, but when transferred to downstream dexterous-hand tasks, high-dimensional hand control, dense contact, and covariate shift make pure offline post-training difficult to converge stably.
- Traditional HiL/DAgger-style correction mostly covers only robot arms or parallel grippers, and cannot provide unified, continuous, fine-grained takeover for both the robot arm and the dexterous hand, resulting in insufficient quality and coordination of corrective data.
- This matters because small errors in dexterous manipulation can accumulate rapidly and enter OOD states, directly affecting reliable deployment on real robots for complex tasks such as grasping and extraction.
Approach
- Proposes an integrated arm-hand HiL teleoperation system: the robot arm uses an ArUco cube for 6D pose mapping, while the hand uses glove keypoints to drive learned joint retargeting, enabling instant online human takeover.
- Designs a two-stage hand retargeting process: first learning a stable motion manifold for the four fingers, then freezing the four fingers and optimizing thumb residuals plus inter-finger geometric constraints, preventing unified five-finger learning from degenerating into “pinch-style” grasping.
- Uses asynchronous multithreaded control: the policy executes autonomously at 20Hz, with human arm control at 30Hz and hand control at 90Hz; when imminent failure is detected, the human takes over and generates corrective trajectories.
- Applies intervention-aware reweighting during training: scarce but high-value human corrective segments are given higher weight in the loss, with the goal of increasing the intervention sample ratio to 0.5 so the model can learn recovery and error-avoidance behaviors more quickly.
- Combines an offline warm-up + online iterative aggregation data pipeline, and filters for recovery segments from “the last intervention to task completion,” reducing distribution conflict and policy oscillation caused by earlier erroneous actions.
Results
- On the Tissue Extraction task, DexHiL reaches a 95% success rate in round 3, outperforming DAgger* at 80% and the offline baseline at 75%.
- On the Plush Toy Grasping task, DexHiL reaches a 65% success rate in round 3, while DAgger* achieves only 20%, and the offline baseline is 35%.
- The abstract states that, relative to the standard offline-only finetuning baseline, DexHiL delivers an average 25% improvement in success rate across different tasks.
- The introduction also states that after 3 rounds of online optimization, compared with offline training baselines using the same amount of data, the two tasks achieve 20% and 30% success-rate improvements, respectively.
- The experimental setup shows: an initial 60 offline trajectories are used for warm-up; afterward, 10 trajectories per task are added each round, and compared against Offline-40/50/60 baselines with equal data budgets; each task is evaluated with 20 independent trials on a real robot.
- The paper also claims that ablation results show the intervention-aware reweighting mechanism is the key driver for breaking the sample-efficiency bottleneck, but the excerpt does not provide a more complete ablation table.
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