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Yebin Wang

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7 papers

1 author row

IROS Conference 2025 Conference Paper

Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations

Haitong Ma
Bo Dai 0001
Zhaolin Ren
Yebin Wang
Na Li 0002

Limited data has become a major bottleneck in scaling up offline imitation learning (IL). In this paper, we propose enhancing IL performance under limited expert data by introducing a pre-training stage that learns dynamics representations, derived from factorizations of the transition dynamics. We first theoretically justify that the optimal decision variable of offline IL lies in the representation space, significantly reducing the parameters to learn in the downstream IL. Moreover, the dynamics representations can be learned from arbitrary data collected with the same dynamics, allowing the reuse of massive non-expert data and mitigating the limited data issues. We present a tractable loss function inspired by noise contrastive estimation to learn the dynamics representations at the pre-training stage. Experiments on MuJoCo demonstrate that our proposed algorithm can mimic expert policies with as few as a single trajectory. Experiments on real quadrupeds show that we can leverage pre-trained dynamics representations from simulator data to learn to walk from a few real-world demonstrations.

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ICRA Conference 2025 Conference Paper

Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion

Ziyi Zhou
Stefano Di Cairano
Yebin Wang
Karl Berntorp

In this paper we address the simultaneous collision detection and force estimation problem for quadrupedal locomotion using joint encoder information and the robot dynamics only. We design an interacting multiple-model Kalman filter (IMM-KF) that estimates the external force exerted on the robot and multiple possible contact modes. The method is invariant to any gait pattern design. Our approach leverages pseudo-measurement information of the external forces based on the robot dynamics and encoder information. Based on the estimated contact mode and external force, we design a reflex motion and an admittance controller for the swing leg to avoid collisions by adjusting the leg's reference motion. Additionally, we implement a force-adaptive model predictive controller to enhance balancing. Simulation ablatation studies and experiments show the efficacy of the approach.

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ICRA Conference 2022 Conference Paper

Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

Jessica EnShiuan Leu
Yebin Wang
Masayoshi Tomizuka
Stefano Di Cairano

This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing.

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IROS Conference 2022 Conference Paper

Improved A-Search Guided Tree for Autonomous Trailer Planning

Jessica EnShiuan Leu
Yebin Wang
Masayoshi Tomizuka
Stefano Di Cairano

This paper presents a motion planning strategy that utilizes the improved A -search guided tree to enable autonomous parking of a general 3-trailer with a car-like tractor. Different from the state-of-the-art state-lattice-based methods, where numerous motion primitives are necessary to ensure successful planning, our work allows quick off-lattice exploration to find a solution. Our treatment brings at least three advantages: fewer and lower design complexity of motion primitives, improved success rate, and increased path quality. Unlike on-lattice exploration, where the cost-to-go is obtained by querying a heuristic look-up table, off-lattice exploration entails the heuristic function being well-defined at off-lattice nodes. We train a neural network through reinforcement learning to model the maneuver costs of the trailer and use it as the heuristic value to better approximate the cost-to-go. Simulations demonstrate the effectiveness of the proposed method in terms of planning speed and path length.

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ICRA Conference 2021 Conference Paper

Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information

Siyu Dai
Yebin Wang

This paper presents a hierarchical motion planning approach that can provide real-time parking plans for autonomous vehicles with limited memory. Through combining a high-level route planner that searches for collision-free routes given traffic and obstacle information and a low level motion planner that considers vehicle dynamics, our approach generates smooth trajectories with reasonable parking behaviors rapidly with very low memory consumption. This hierarchical approach allows for online path repairing and replanning when newly detected obstacles that were not indicated on the offline map obstruct the original planned trajectory. It employs a fast clearance checking procedure to obtain a practical indicator of repairability as well as heuristic guidance for rapid trajectory repairing, and utilizes the high-level route planner to conduct real-time replanning when trajectory repairing is deemed to be difficult. Performance analysis on parking tasks in simulation environments demonstrates the advantages of the proposed approach in terms of both trajectory quality and planning time.

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ICRA Conference 2019 Conference Paper

Improved A-search guided tree construction for kinodynamic planning

Yebin Wang

With node selection being directed by a heuristic cost [1]-[3], A-search guided tree (AGT) is constructed on-the-fly and enables fast kinodynamic planning. This work presents two variants of AGT to improve computation efficiency. An improved AGT (i-AGT) biases node expansion through prioritizing control actions, an analogy of prioritizing nodes. Focusing on node selection, a bi-directional AGT (BAGT) introduces a second tree originated from the goal in order to offer a better heuristic cost of the first tree. Effectiveness of BAGT pivots on the fact that the second tree encodes obstacles information near the goal. Case study demonstrates that i-AGT consistently reduces the complexity of the tree and improves computation efficiency; and BAGT works largely but not always, particularly with no benefit observed for simple cases.

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ICAPS Conference 2019 Conference Paper

Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks

Takeshi Takahashi
He Sun 0010
Dong Tian
Yebin Wang

Resorting to certain heuristic functions to guide the search, the computational efficiency of prevailing path planning algorithms, such as A*, D*, and their variants, is solely determined by how well the heuristic function approximates the true path cost. In this study, we propose a novel approach to learning heuristic functions using a deep neural network (DNN) to improve the computational efficiency. Even though DNNs have been widely used for object segmentation, natural language processing, and perception, their role in helping to solve path planning problems has not been well investigated. This work shows how DNNs can be applied to path planning and what kind of loss functions are suitable for learning such a heuristic. Our preliminary results show that an appropriately designed and trained DNN can learn a heuristic that effectively guides prevailing path planning algorithms.

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