robotics
The Robot Learned 'Gently' as an Execution Control
Modifier-conditioned decoding improved force-direction following on a real whiteboard-wiping task while retaining speed control.
Summary
Modifier-conditioned decoding improved force-direction following on a real whiteboard-wiping task while retaining speed control.
Contact-rich imitation learning usually reproduces an action without giving the operator a direct way to ask for slower, faster, gentler or firmer execution. Bi-MoDe injects a constrained modifier latent into every layer of a Transformer action decoder, allowing directives to alter action chunks. On a physical whiteboard-wiping task with combinations of temporal and force modifiers, it improved physical-directive following over the action-chunking baseline while maintaining comparable temporal control. The experiment demonstrates one task and robot setup, not a general natural-language safety interface.
Why it matters
Modifier-conditioned decoding improved force-direction following on a real whiteboard-wiping task while retaining speed control.
Limits and context
- The experiment demonstrates one task and robot setup, not a general natural-language safety interface.
Key claims
Modifier-conditioned decoding improved force-direction following on a real whiteboard-wiping task while retaining speed control.
Qualification: The experiment demonstrates one task and robot setup, not a general natural-language safety interface.
Evidence: source-2026-09-16-011
Sources
- arXiv preprint 2609.16040arXiv · primary research
Corrections
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