BlenDAgger: Blended Shared Control for Interactive Imitation Learning

Learning from demonstrations is effective for robotic manipulation, but collecting sufficient task-specific data remains a major bottleneck. Under distribution shift, small errors compound, performance degrades, and expert time is often spent on redundant, low-value corrections instead of the few critical failure cases. We present VR-DAgger, a human-in-the-loop framework centered on an immersive VR application for dexterous teleoperation, demonstration collection, and selective policy correction. The VR client provides intuitive hand control with synchronized scene visualization, while a backend workstation runs simulation and learning, enabling autonomous rollouts without continuous operator oversight. We use Monte Carlo (MC) dropout to score uncertainty during Isaac Lab rollouts of a diffusion policy and select informative failure segments for correction. These segments are replayed in VR as clips, where the operator selectively labels and corrects the policy's behavior, concentrating supervision where uncertainty is highest without full-rollout monitoring or a separate intervention classifier. We evaluate on three dexterous manipulation tasks (Pan pick-and-place, Drawer opening, Valve turning) with a 10-DoF XHand under standard and challenging initial configurations. Active labeling consistently improves over behavioral cloning across all tasks, with gains of up to 23 percentage points. Compared to unguided human-in-the-loop inspection, VR-DAgger reduces per-sample collection time by approximately 40% by focusing review on selected segments rather than full rollouts.

Large behaviour models have transformed the field of robotic manipulation, but prohibitive data requirements have thus far prevented a revolution similar to vision language models. We believe that instrumentation, i.e. sensor integration in objects, can provide invaluable state information and enable efficient learning for robotic manipulation. In this paper, we present instrumented imitation learning of clothes hanger insertion. Using 180 teleoperated demonstrations, we train diffusion policies with and without access to instrumentation data. Results show that policies leveraging instrumentation outperform vision-only counterparts by 14-25 %pt and exhibit greater task awareness. Crucially, a black-box imitation learning policy learns to prioritise instrumentation signals without explicit guidance. In addition, enhancing the teleoperation dataset with rollouts from an instrumented expert policy, enables a vision-only student policy to achieve performance comparable to the instrumented expert, thereby surpassing the original vision-only policy. These findings establish instrumentation as a promising strategy to enhance imitation learning for robotic manipulation. Datasets are available on Zenodo.

Behavior cloning for robot manipulation relies on expert demonstrations. However, for tasks that require dynamic stability, precise contact timing, or dexterous coordination, human operators may find it hard or even impossible to collect data. We study this infeasible-demonstration regime and propose GLIDE: Guardrails for Learning from Infeasible Demonstrations Efficiently, a framework that infers task-specific failure modes and converts them into executable guardrails for data collection and policy deployment. Given a task description and the conditioning teleoperation code, GLIDE writes guardrails that use system states to filter teleoperation and policy commands, constrain failure-prone actions, and iteratively improve from trajectory feedback. Across three tasks, GLIDE discovers emergent guardrails that go beyond domain-expert hardcoded ones, improving data collection over naive VR teleoperation and domain-expert hardcoded guardrails. After refinement, GLIDE raises data-collection success from 0-10 percent to 70-90 percent across the three tasks. During policy execution, mixed-data guarded policies reach 70 percent, 60 percent, and 60 percent success on Tomato plate transfer, Marker handover and stand, and Wine serving tasks. These results show that GLIDE can support policy learning when direct demonstrations are infeasible. Project website: http://guardrail-policy.github.io/