SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly

• URL: http://arxiv.org/abs/2602.23253v1
• Date: Thu, 26 Feb 2026 17:26:13 GMT
• Title: SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly
• Abstract summary: We propose a hybrid approach that combines a simulation-trained base policy with a real-world residual policy.<n>Our method achieves near-perfect success rates across diverse two-part assembly tasks.
• Score: 13.3674466451862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of robust assembly policies, their performance often degrades when deployed in real-world settings due to the sim-to-real gap. Conversely, real-world reinforcement learning (RL) methods avoid the sim-to-real gap, but rely heavily on human supervision and lack generalization ability to environmental changes. In this work, we propose a hybrid approach that combines a simulation-trained base policy with a real-world residual policy to efficiently adapt to real-world variations. The base policy, trained in simulation using low-level state observations and dense rewards, provides strong priors for initial behavior. The residual policy, learned in the real world using visual observations and sparse rewards, compensates for discrepancies in dynamics and sensor noise. Extensive real-world experiments demonstrate that our method, SPARR, achieves near-perfect success rates across diverse two-part assembly tasks. Compared to the state-of-the-art zero-shot sim-to-real methods, SPARR improves success rates by 38.4% while reducing cycle time by 29.7%. Moreover, SPARR requires no human expertise, in contrast to the state-of-the-art real-world RL approaches that depend heavily on human supervision.

Related papers

• Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement [52.93267443851685]
  Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks.<n>Can a reinforcement learning policy trained purely in simulation improve the robustness of real-world VLAs zero-shot?<n>We propose an object-centric residual RL framework that refines VLA actions using object poses.
  arXiv  Detail & Related papers  (2026-06-17T11:36:54Z)
• LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning [90.86828952599147]
  We present LaST-R1, a novel reinforcement learning framework designed to harness "latent reasoning-before-acting" policies.<n>LaST-R1 achieves a near-perfect 99.9% average success rate on the LIBERO benchmark.<n>In real-world deployments, LaST-R1 yields up to a 22.5% average improvement over SOTA supervised fine-tuning approach.
  arXiv  Detail & Related papers  (2026-04-30T17:59:52Z)
• PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies [88.78188489161028]
  We introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS)<n>PolaRiS is a scalable real-to-sim framework for high-fidelity simulated robot evaluation.<n>We show that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks.
  arXiv  Detail & Related papers  (2025-12-18T18:49:41Z)
• Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer [59.02729900344616]
  GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning.<n>We develop a teacher-student-bootstrap learning framework for vision-based humanoid loco-manipulation.<n>This represents the first humanoid sim-to-real policy capable of diverse articulated loco-manipulation using pure RGB perception.
  arXiv  Detail & Related papers  (2025-11-30T20:07:13Z)
• 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.<n>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)
• LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots [20.715834172041763]
  We propose LoopSR, a lifelong policy adaptation framework that continuously refines RL policies in the post-deployment stage.<n>LoopSR employs a transformer-based encoder to map real-world trajectories into a latent space.<n>Autoencoder architecture and contrastive learning methods are adopted to enhance feature extraction of real-world dynamics.
  arXiv  Detail & Related papers  (2024-09-26T16:02:25Z)
• 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)
• 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)
• Marginalized Importance Sampling for Off-Environment Policy Evaluation [13.824507564510503]
  Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. This paper proposes a new approach to evaluate the real-world performance of agent policies prior to deploying them in the real world. Our approach incorporates a simulator along with real-world offline data to evaluate the performance of any policy.
  arXiv  Detail & Related papers  (2023-09-04T20:52:04Z)
• Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning [0.8399688944263843]
  Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. Sim-to-real approaches leverage simulations to pretrain DRL policies and then deploy them in the real world. This work proposes a distributed cloud-edge architecture to train DRL agents in the real world in real-time.
  arXiv  Detail & Related papers  (2022-03-04T10:27:01Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• 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)

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.