HIRE: History-Conditioned Interaction Reasoning and High-Rate Execution for Visually Aliased Precision Manipulation

2026-09-25

AI Summary

This work addresses the decision ambiguity and execution errors in history-dependent dexterous manipulation caused by visual aliasing, proposing the HIRE framework. HIRE pioneers the decoupling and unification of long-horizon physical evidence reasoning with high-frequency contact execution. By incorporating a temporal torque encoder, a Force Perceiver, and a decomposition strategy for intrinsic progress and lateral correction, it achieves cross-rate closed-loop fusion and state-consistent action generation. Experimental results demonstrate that the proposed method attains stage-wise completion rates exceeding 90% across surface, insertion, and rotation tasks, significantly enhancing state disambiguation capabilities and generalization performance.

Abstract

Precision manipulation with contact-critical interactions is often history-dependent: visually similar observations can correspond to different latent interaction states and therefore require different actions, while small execution errors can alter task outcomes. Policies relying on the current visual observation alone cannot resolve such ambiguity; force-aware and memory-augmented methods enrich physical or temporal context, while reactive high-rate policies improve local contact response, yet long-horizon temporal reasoning and precision execution remain largely decoupled in existing methods, limiting reliable progression in visually aliased precision manipulation. To bridge this gap, we introduce History-Conditioned Interaction Reasoning and Execution (HIRE), a cross-rate framework comprising a history-conditioned Interaction-State Reasoner (ISR) and a high-rate Interaction-Manifold Executor (IME). ISR encodes ordered wrench history with a temporal wrench encoder and Force Perceiver as persistent physical evidence for state-consistent action generation, while IME structures contact-critical motion into intrinsic progress and transverse correction for precise execution; their cross-rate loop allows the resulting physical traces to inform subsequent reasoning. In real-robot experiments across surface, insertion, and rotational interactions, HIRE achieves at least 90% completion across all evaluated task stages while improving interaction-state disambiguation, execution precision, and generalization. More broadly, HIRE provides a unified reasoning--execution perspective on precision manipulation under history-dependent partial observability, where physical interaction both realizes task intent and reveals latent-state evidence for future decisions. Code will be released upon publication.

Problem

• precision manipulation
• visual aliasing
• history-dependent interaction
• temporal reasoning
• high-rate execution

Innovation

• History-Conditioned Reasoning
• Cross-Rate Framework
• Precision Manipulation
• Interaction-State Disambiguation
• Wrench Encoding