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PhaForce: Phase-Scheduled Visual-Force Policy Learning with Slow Planning and Fast Correction for Contact-Rich Manipulation

2026-03-09 · Mingxin Wang, Zhirun Yue, Renhao Lu, Yizhe Li, Zihan Wang, Guoping Pan, Kangkang Dong, Jun Cheng, Yi Cheng, Houde Liu arxiv

Contact-rich manipulation requires not only vision-dominant task semantics but also closed-loop reactions to force/torque (F/T) transients. Yet, generative visuomotor policies are typically constrained to low-frequency updates due to inference latency and action chunking, underutilizing F/T for control-rate feedback. Furthermore, existing force-aware methods often inject force continuously and indiscriminately, lacking an explicit mechanism to schedule when / how much / where to apply force across different task phases. We propose PhaForce, a phase-scheduled visual--force policy that coordinates low-rate chunk-level planning and high-rate residual correction via a unified contact/phase schedule. PhaForce comprises (i) a contact-aware phase predictor (CAP) that estimates contact probability and phase belief, (ii) a Slow diffusion planner that performs dual-gated visual--force fusion with orthogonal residual injection to preserve vision semantics while conditioning on force, and (iii) a Fast corrector that applies control-rate phase-routed residuals in interpretable corrective subspaces for within-chunk micro-adjustments. Across multiple real-robot contact-rich tasks, PhaForce achieves an average success rate of 86% (+40 pp over baselines), while also substantially improving contact quality by regulating interaction forces and exhibiting robust adaptability to OOD geometric shifts.

📄 PDF Abstract BibTeX arXiv:2603.08342

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Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

2018-06-16 · Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang 외

We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs. In contrast to existing methods which start with learning from demonstrations…

Efficient Exploration reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Cross-Modal Subspace Learning with Scheduled Adaptive Margin Constraints

2019-09-30 · David Semedo, João Magalhães

Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations. State-of-the-art approaches use maximum-margin methods, based on the hinge-loss, to enfo…

Incremental Learning Triplet

Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning

2024-01-31 · Xuecheng Niu, Akinori Ito, Takashi Nose

Training task-oriented dialog agents based on reinforcement learning is time-consuming and requires a large number of interactions with real users. How to grasp dialog policy within limited dialog experiences remains an …

Efficient Exploration Model-based Reinforcement Learning Q-Learning reinforcement-learning

Lenient Multi-Agent Deep Reinforcement Learning

2017-07-14 · Gregory Palmer, Karl Tuyls, Daan Bloembergen, Rahul Savani

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration

2019-03-18 · Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker 외

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities …

Reinforcement Learning