CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding
We introduce CAPABLE, a unified capability-aware adaptation framework for frozen VLAs that integrates self-supervised capability inference with residual reinforcement learning.
ProofPaper ↗
Key points
- Vision-language-action (VLA) policies assume the embodiment on which they were trained and can fail when a joint fault changes how commanded actions are physically executed.
- Existing fault-recovery methods often require task-specific retraining, fault labels, explicit diagnosis, or privileged embodiment information.
- CAPABLE infers capability, how much of the commanded motion each joint actually realizes and how that motion contributes to end-effector behavior, online from command-response history and kinematics using a temporal encoder shared across joints, Jacobian grounding, cross-joint attention, and self-supervised physical prediction.
- The resulting representation conditions a residual policy that adds bounded corrections to the VLA arm action without fault labels or faulty-joint identifiers.
Sources (1)
- [1]CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent EncodingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:46 PM
We introduce CAPABLE, a unified capability-aware adaptation framework for frozen VLAs that integrates self-supervised capability inference with residual reinforcement learning.
Vision-language-action (VLA) policies assume the embodiment on which they were trained and can fail when a joint fault changes how commanded actions are physically executed.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 8, 2026Rewiring Semantics, Dynamics, and Control: A Simple yet Effective Action-Centric Tri-Stream Transformer
- Oct 8, 2026Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement
- Oct 7, 2026Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving
- Oct 7, 2026Do Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding
- Oct 7, 2026Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization
- Oct 7, 2026Q-Learning with Scalar Adjoint Matching