Selected work

Representative Papers

Robot Planning and Situation Handling with Active Perception

Apr 28, 2026

This work addresses the challenge of task execution failures in dynamic, open-world environments—such as those caused by jammed doors or unforeseen ground obstacles—by introducing the VAP-TAMP framework. VAP-TAMP uniquely integrates action-knowledge-guided active viewpoint selection with vision-language models and leverages scene graph construction and reasoning to enable joint task and motion planning (TAMP). The proposed approach facilitates real-time detection of and response to execution anomalies, significantly improving both task success rates and robotic autonomy in complex, dynamic settings. Evaluations on both simulated and real-world service robot platforms demonstrate its effectiveness in enhancing robustness and adaptability under uncertainty.

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No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets

Aug 16, 2025

Traditional velocity-tracking controllers for short-range SE(2) pose navigation in humanoid robots induce inefficient, “marching-like” locomotion. Method: This paper proposes an end-to-end reinforcement learning framework that directly optimizes pose reaching—bypassing intermediate velocity trajectory tracking. We introduce a sparse reward function based on a constellation-inspired geometric structure to encourage natural, energy-efficient target-oriented motion; design a multi-objective evaluation benchmark integrating energy consumption, task completion time, and step count; and employ SE(2)-encoded goal representations with curriculum learning to enhance sim-to-real policy transfer. Contribution/Results: Experiments demonstrate significant improvements over baseline methods across all metrics, including reduced energy use, shorter execution time, and fewer steps. The learned policy is successfully deployed on a real humanoid robot platform, validating its practical efficacy and generalizability.

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Recent publications

Latest Papers

Robot Planning and Situation Handling with Active Perception

Apr 28, 2026

This work addresses the challenge of task execution failures in dynamic, open-world environments—such as those caused by jammed doors or unforeseen ground obstacles—by introducing the VAP-TAMP framework. VAP-TAMP uniquely integrates action-knowledge-guided active viewpoint selection with vision-language models and leverages scene graph construction and reasoning to enable joint task and motion planning (TAMP). The proposed approach facilitates real-time detection of and response to execution anomalies, significantly improving both task success rates and robotic autonomy in complex, dynamic settings. Evaluations on both simulated and real-world service robot platforms demonstrate its effectiveness in enhancing robustness and adaptability under uncertainty.

0 citationsRead paper

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets

Aug 16, 2025

Traditional velocity-tracking controllers for short-range SE(2) pose navigation in humanoid robots induce inefficient, “marching-like” locomotion. Method: This paper proposes an end-to-end reinforcement learning framework that directly optimizes pose reaching—bypassing intermediate velocity trajectory tracking. We introduce a sparse reward function based on a constellation-inspired geometric structure to encourage natural, energy-efficient target-oriented motion; design a multi-objective evaluation benchmark integrating energy consumption, task completion time, and step count; and employ SE(2)-encoded goal representations with curriculum learning to enhance sim-to-real policy transfer. Contribution/Results: Experiments demonstrate significant improvements over baseline methods across all metrics, including reduced energy use, shorter execution time, and fewer steps. The learned policy is successfully deployed on a real humanoid robot platform, validating its practical efficacy and generalizability.

0 citationsRead paper