AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer

• URL: http://arxiv.org/abs/2302.04903v2
• Date: Sun, 1 Oct 2023 03:41:45 GMT
• Title: AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
• Authors: Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel, Anirudha Majumdar
• Abstract summary: AdaptSim aims to optimize task performance in target (real) environments. First, we meta-learn an adaptation policy in simulation using reinforcement learning. We then perform iterative real-world adaptation by inferring new simulation parameter distributions for policy training.
• Score: 10.173835871228718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to real-world deployment. Previous approaches typically handcraft distributions over such parameters (domain randomization), or identify parameters that best match the dynamics of the real environment (system identification). However, there is often an irreducible gap between simulation and reality: attempting to match the dynamics between simulation and reality across all states and tasks may be infeasible and may not lead to policies that perform well in reality for a specific task. Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality. First, we meta-learn an adaptation policy in simulation using reinforcement learning for adjusting the simulation parameter distribution based on the current policy's performance in a target environment. We then perform iterative real-world adaptation by inferring new simulation parameter distributions for policy training, using a small amount of real data. We perform experiments in three robotic tasks: (1) swing-up of linearized double pendulum, (2) dynamic table-top pushing of a bottle, and (3) dynamic scooping of food pieces with a spatula. Our extensive simulation and hardware experiments demonstrate AdaptSim achieving 1-3x asymptotic performance and $\sim$2x real data efficiency when adapting to different environments, compared to methods based on Sys-ID and directly training the task policy in target environments. Website: https://irom-lab.github.io/AdaptSim/

Related papers

• Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications [23.94013806312391]
  We propose a novel approach that dynamically adjusts simulation environment parameters online using in-context learning. We validate our approach across two tasks: object scooping and table air hockey. Our approach delivers efficient and smooth system identification, advancing the deployment of robots in dynamic real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-27T07:13:38Z)
• 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)
• 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)
• DrEureka: Language Model Guided Sim-To-Real Transfer [64.14314476811806]
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball.
  arXiv  Detail & Related papers  (2024-06-04T04:53:05Z)
• Evaluating Real-World Robot Manipulation Policies in Simulation [91.55267186958892]
  Control and visual disparities between real and simulated environments are key challenges for reliable simulated evaluation. We propose approaches for mitigating these gaps without needing to craft full-fidelity digital twins of real-world environments. We create SIMPLER, a collection of simulated environments for manipulation policy evaluation on common real robot setups.
  arXiv  Detail & Related papers  (2024-05-09T17:30:16Z)
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation [79.66851068682779]
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR)
  arXiv  Detail & Related papers  (2023-07-28T05:47:24Z)
• Zero-shot Sim2Real Adaptation Across Environments [45.44896435487879]
  We propose a Reverse Action Transformation (RAT) policy which learns to imitate simulated policies in the real-world. RAT can then be deployed on top of a Universal Policy Network to achieve zero-shot adaptation to new environments.
  arXiv  Detail & Related papers  (2023-02-08T11:59:07Z)
• Auto-Tuned Sim-to-Real Transfer [143.44593793640814]
  Policies trained in simulation often fail when transferred to the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering. We propose a method for automatically tuning simulator system parameters to match the real world.
  arXiv  Detail & Related papers  (2021-04-15T17:59:55Z)
• 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)
• Reactive Long Horizon Task Execution via Visual Skill and Precondition Models [59.76233967614774]
  We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We show an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines.
  arXiv  Detail & Related papers  (2020-11-17T15:24:01Z)
• Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models [3.7692466417039814]
  We propose a method to predict the sim-to-real transfer performance of RL policies. A probabilistic dynamics model is trained alongside the policy and evaluated on a fixed set of real-world trajectories.
  arXiv  Detail & Related papers  (2020-09-27T15:06:54Z)

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.