AutoSERL achieves strong performance on six real-world robot manipulation tasks using RL guided by a single demonstration via sliding-window intervention, safety recovery, and automatic termination.
Compliant residual dagger: Improving real-world contact-rich manipulation with human corrections
10 Pith papers cite this work. Polarity classification is still indexing.
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MuSe adapts a vision-action policy to force-torque via multi-stage fusion, multisensory future prediction, and experience replay, improving both new contact-rich tasks and original vision tasks.
ReGuide is a self-improving framework that uses phase-conditioned guidance to generate corrective rollouts and absorbs successful ones back into diffusion policy training, yielding 1.3-7.7x success gains on Robomimic tasks.
UniIntervene uses future-conditioned action-value estimation and a temporal value-risk critic to trigger memory-based recovery interventions, reporting 8.6% higher success rates and 57% fewer human interventions than prior HiL-RL methods on real manipulation tasks.
HandITL enables seamless human intervention in VLA policies for bimanual dexterous manipulation, cutting jitter by 99.8% and improving refined policies by 19% over standard teleoperation.
WM-DAgger uses world models with corrective action synthesis and consistency-guided filtering to aggregate OOD recovery data for imitation learning, reporting 93.3% success in soft bag pushing with five demonstrations.
TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.
BRIDGE routes between handheld and teleoperated diffusion policy experts via robot state to achieve up to 36.7% higher success rates than handheld-only baselines on three contact-rich tasks.
IMPACT decouples forceful manipulation into task-planning and internal-model predictive control, claiming higher success rates, better generalization to unseen weights, and improved safety and energy efficiency in simulation and real-world tests.
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.
citing papers explorer
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One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
AutoSERL achieves strong performance on six real-world robot manipulation tasks using RL guided by a single demonstration via sliding-window intervention, safety recovery, and automatic termination.
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Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force
MuSe adapts a vision-action policy to force-torque via multi-stage fusion, multisensory future prediction, and experience replay, improving both new contact-rich tasks and original vision tasks.
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ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies
ReGuide is a self-improving framework that uses phase-conditioned guidance to generate corrective rollouts and absorbs successful ones back into diffusion policy training, yielding 1.3-7.7x success gains on Robomimic tasks.
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UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
UniIntervene uses future-conditioned action-value estimation and a temporal value-risk critic to trigger memory-based recovery interventions, reporting 8.6% higher success rates and 57% fewer human interventions than prior HiL-RL methods on real manipulation tasks.
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Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention
HandITL enables seamless human intervention in VLA policies for bimanual dexterous manipulation, cutting jitter by 99.8% and improving refined policies by 19% over standard teleoperation.
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WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models
WM-DAgger uses world models with corrective action synthesis and consistency-guided filtering to aggregate OOD recovery data for imitation learning, reporting 93.3% success in soft bag pushing with five demonstrations.
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TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks
TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.
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Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts
BRIDGE routes between handheld and teleoperated diffusion policy experts via robot state to achieve up to 36.7% higher success rates than handheld-only baselines on three contact-rich tasks.
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IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
IMPACT decouples forceful manipulation into task-planning and internal-model predictive control, claiming higher success rates, better generalization to unseen weights, and improved safety and energy efficiency in simulation and real-world tests.
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Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.