Neural Fidelity Calibration for Informative Sim-to-Real Adaptation

• URL: http://arxiv.org/abs/2504.08604v1
• Date: Fri, 11 Apr 2025 15:12:12 GMT
• Title: Neural Fidelity Calibration for Informative Sim-to-Real Adaptation
• Authors: Youwei Yu, Lantao Liu,
• Abstract summary: Deep reinforcement learning can seamlessly transfer agile locomotion and navigation skills from the simulator to real world.<n>However, bridging the sim-to-real gap with domain randomization or adversarial methods often demands expert physics knowledge to ensure policy robustness.<n>We propose Neural Fidelity (NFC), a novel framework that employs conditional score-based diffusion models to calibrate simulator physical coefficients and residual fidelity domains online during robot execution.
• Score: 10.117298045153564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning can seamlessly transfer agile locomotion and navigation skills from the simulator to real world. However, bridging the sim-to-real gap with domain randomization or adversarial methods often demands expert physics knowledge to ensure policy robustness. Even so, cutting-edge simulators may fall short of capturing every real-world detail, and the reconstructed environment may introduce errors due to various perception uncertainties. To address these challenges, we propose Neural Fidelity Calibration (NFC), a novel framework that employs conditional score-based diffusion models to calibrate simulator physical coefficients and residual fidelity domains online during robot execution. Specifically, the residual fidelity reflects the simulation model shift relative to the real-world dynamics and captures the uncertainty of the perceived environment, enabling us to sample realistic environments under the inferred distribution for policy fine-tuning. Our framework is informative and adaptive in three key ways: (a) we fine-tune the pretrained policy only under anomalous scenarios, (b) we build sequential NFC online with the pretrained NFC's proposal prior, reducing the diffusion model's training burden, and (c) when NFC uncertainty is high and may degrade policy improvement, we leverage optimistic exploration to enable hallucinated policy optimization. Our framework achieves superior simulator calibration precision compared to state-of-the-art methods across diverse robots with high-dimensional parametric spaces. We study the critical contribution of residual fidelity to policy improvement in simulation and real-world experiments. Notably, our approach demonstrates robust robot navigation under challenging real-world conditions, such as a broken wheel axle on snowy surfaces.

Related papers

• Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator [50.191655141020505]
  Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap. We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates uncertainty to improve policy learning without reliance on a physics simulator.
  arXiv  Detail & Related papers  (2025-04-23T12:58:15Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  We introduce a novel framework for learning world models.<n>By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• Scalable Offline Reinforcement Learning for Mean Field Games [6.8267158622784745]
  Off-MMD is a novel mean-field RL algorithm that approximates equilibrium policies in mean-field games using purely offline data. Our algorithm scales to complex environments and demonstrates strong performance on benchmark tasks like crowd exploration or navigation.
  arXiv  Detail & Related papers  (2024-10-23T14:16:34Z)
• LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots [20.715834172041763]
  We propose a lifelong policy adaptation framework named LoopSR. It reconstructs the real-world environments back in simulation for further improvement. By leveraging the continual training, LoopSR achieves superior data efficiency compared with strong baselines.
  arXiv  Detail & Related papers  (2024-09-26T16:02:25Z)
• ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable [88.08120417169971]
  Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data. This work explores generating safety-critical driving scenarios by modifying complex real-world regular scenarios through trajectory optimization. Our approach addresses unrealistic diverging trajectories and unavoidable collision scenarios that are not useful for training robust planner.
  arXiv  Detail & Related papers  (2024-09-12T08:26:33Z)
• Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning [15.792914346054502]
  We tackle the challenge of sim-to-real transfer of reinforcement learning (RL) agents for coverage path planning ( CPP) We bridge the sim-to-real gap through a semi-virtual environment, including a real robot and real-time aspects, while utilizing a simulated sensor and obstacles. We find that a high inference frequency allows first-order Markovian policies to transfer directly from simulation, while higher-order policies can be fine-tuned to further reduce the sim-to-real gap.
  arXiv  Detail & Related papers  (2024-06-07T13:24:19Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning [3.1515473193934778]
  Off-dynamics Reinforcement Learning seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. We propose an innovative approach inspired by recent advancements in Imitation Learning and conservative RL algorithms.
  arXiv  Detail & Related papers  (2023-12-24T13:09:08Z)
• Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots [14.760426243769308]
  In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones.
  arXiv  Detail & Related papers  (2023-03-16T18:49:05Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation [1.290382979353427]
  Soft-Actor Critic (SAC) training strategy using incremental environment complexity is proposed to drastically reduce the need for additional training in the real world. The application addressed is depth-based mapless navigation, where a mobile robot should reach a given waypoint in a cluttered environment with no prior mapping information.
  arXiv  Detail & Related papers  (2020-04-30T10:47:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.