BORA: New Framework Improves Robot Hand Control by Combining Offline Learning with Real-World Adaptation
Two new frameworks—BORA and TacCoRL—have been developed to improve Vision-Language-Action (VLA) models for complex robotic manipulation tasks. BORA uses offline-to-online reinforcement learning with human-in-the-loop adaptation for dexterous hand control, while TacCoRL integrates tactile feedback through simulation-based training. These approaches address key limitations of visual-only policies, achieving significant performance improvements on real-world manipulation tasks.
Recent research presents two complementary approaches to enhancing Vision-Language-Action models for real-world robotic manipulation. BORA (Bridging Offline Reinforcement Learning and Online Residual Adaptation) proposes a two-phase framework where an offline phase constructs a critic using both visual-language tokens and action information, followed by an online phase with lightweight human-in-the-loop residual adaptation to correct execution errors in the physical environment. The framework demonstrates a 33% absolute improvement in success rates on dexterous tasks and up to 43% improvement on unseen object generalization. TacCoRL takes a different approach by integrating tactile feedback into VLA policies through simulation-based co-training and reinforcement learning, avoiding the need for large-scale tactile pretraining or risky real-world contact exploration. By learning how contact readings should modulate actions in near-failure states, TacCoRL achieves 72.5% average success on bimanual contact-rich tasks compared to 50% for baseline approaches. Both frameworks address the gap between visual grounding and physical execution reliability in dexterous manipulation.
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- arXiv cs.AICenter
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
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