Residual Dynamics Learning

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 12

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RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing
Oct 5, 2026Yuxiang Liu, Marla Eisman, Lizhi Yang +2Residual Dynamics Learning Sim-to-Real Transfer
University of California, Berkeley, CA, USA. · California Institute of Technology, Pasadena, CA, USA.
Optimal Control with Learned Critics under Unmodeled State Dependencies
Oct 4, 2026Philipp Schoch, Markus RyllResidual Dynamics Learning Robot Dynamics Learning
Autonomous Aerial Systems Lab Technical University of Munich
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Sep 28, 2026Yichao Liang, Amber Li, Dat Nguyen +13World Models for Robotics Residual Dynamics Learning
Basis Research Institute · University of Cambridge · Carnegie Mellon University +6
Task-Oriented Active Learning of Residual Dynamics for Model Predictive Path Integral Control
Sep 16, 2026Nobuaki Aoki, Hojin Lee, Stefan Sosnowski +1Residual Learning Residual Dynamics Learning
Chair of Information-oriented Control (ITR), Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
Calibrate Once, Fly Any Team: Residual-Grounded Low-Fidelity Training for Cooperative Drone Swarms
Sep 15, 2026Maxim Mednikov, Oren GalAerial Robotics Swarm Robotics
Swarm & AI Lab (SAIL), University of Haifa, Israel
Learning Physics-Guided Residual Dynamics for Deformable Object Simulation
Jul 15, 2026Shivansh Patel, Kaifeng Zhang, Sanjay Pokkali +2Residual Learning Residual Dynamics Learning
UIUC · Columbia University
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
Jul 14, 2026Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1Multi-Robot Formation Control Residual Dynamics Learning
1GRASP Laboratory, University of Pennsylvania, PA, USA
Continual Robot Policy Learning via Variational Neural Dynamics
Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy Learning Robot Policy Adaptation
Robotics and Perception Group, University of Zurich
Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
Jun 8, 2026Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2Robotic Control Robot Dynamics Identification
Department of Computer Science, The University of Manchaster, U.K. · Department of Cognitive Robotics, Delft University of Technology, Netherlands · International Institute of Information Technology Hyderabad. +1
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
May 14, 2026Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2Aerial Robotics Online System Identification
Department of Computer Science, The University of Manchaster, U.K. · Department of Cognitive Robotics, Delft University of Technology, Netherlands · International Institute of Information Technology Hyderabad. +1
Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
Apr 16, 2026Johannes Kübel, Henrik Krauss, Jinjie Li +1Aerial Robotics Residual Dynamics Learning
DRAGON Lab, Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8656, Japan · Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo 153-8904, Japan
Model Predictive Path Integral PID Control for Learning-Based Path Following
Mar 31, 2026Teruki Kato, Koshi Oishi, Seigo ItoNonlinear MPC Residual Dynamics Learning
Toyota Central R&D Labs., Inc., Nagakute 480-1192, Japan

Oct 5, 2026, cs.ROJ/K move · Enter open · S save

RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing

Yuxiang Liu, Marla Eisman, Lizhi Yang, Aaron Ames+1

University of California, Berkeley, CA, USA. · California Institute of Technology, Pasadena, CA, USA.

Residual Dynamics Learning Sim-to-Real Transfer Low-Rank Adaptation Quadruped Locomotion

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We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model and a low-level RL velocity tracker. The planner augments a nominal unicycle kinematic model with a neural residual term to capture the closed-loop tracking behavior of the RL policy. To train this residual model under limited real-world data, we propose Low-Rank Residual Adaptation (LoRRA), a two-stage approach that pre-trains on large-scale simulation data for broad coverage and then fine-tunes on a small real-world dataset with a low-rank constraint. In simulation, we empirically validate our engineering choices by showing (A) Residual dynamics improve the overall performance of our pipeline by capturing the tracking error of RL velocity tracker at high-speed cornering. (B) Residual dynamics trained with both source-domain and target-domain data gives racing performance significantly better than the residual dynamics trained with only target-domain data. (C) Low-rank constraint at target-domain adaptation gives higher success rates and higher performance than full-tune and from-scratch when domain gap in ground coefficient or joint gain increases.

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