ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation

• URL: http://arxiv.org/abs/2506.04646v1
• Date: Thu, 05 Jun 2025 05:28:14 GMT
• Title: ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
• Authors: Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas,
• Abstract summary: Planning with learned dynamics models offers a promising approach toward real-world, long-horizon manipulation.<n>ActivePusher is a framework that combines residual-physics modeling with kernel-based uncertainty-driven active learning.<n>We evaluate our approach in both simulation and real-world environments and demonstrate that it improves data efficiency and planning success rates compared to baseline methods.
• Score: 2.7405276609125164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Planning with learned dynamics models offers a promising approach toward real-world, long-horizon manipulation, particularly in nonprehensile settings such as pushing or rolling, where accurate analytical models are difficult to obtain. Although learning-based methods hold promise, collecting training data can be costly and inefficient, as it often relies on randomly sampled interactions that are not necessarily the most informative. To address this challenge, we propose ActivePusher, a novel framework that combines residual-physics modeling with kernel-based uncertainty-driven active learning to focus data acquisition on the most informative skill parameters. Additionally, ActivePusher seamlessly integrates with model-based kinodynamic planners, leveraging uncertainty estimates to bias control sampling toward more reliable actions. We evaluate our approach in both simulation and real-world environments and demonstrate that it improves data efficiency and planning success rates compared to baseline methods.

Related papers

• Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning [49.10890099624699]
  We introduce a dynamic dataset pruning framework that adaptively selects training samples based on task-driven difficulty and cross-modality semantic consistency.<n>Our work highlights the potential of integrating cross-modality alignment for robust sample selection, advancing data-centric learning toward more efficient and robust practices across application domains.
  arXiv  Detail & Related papers  (2025-07-17T03:08:26Z)
• Cost-effective Reduced-Order Modeling via Bayesian Active Learning [12.256032958843065]
  We propose BayPOD-AL, an active learning framework based on an uncertainty-aware Bayesian proper decomposition (POD) approach.<n> Experimental results on predicting the temperature evolution over a rod demonstrate BayPOD-AL's effectiveness in suggesting the informative data.<n>We demonstrate BayPOD-AL's generalizability and efficiency by evaluating its performance on a dataset of higher temporal resolution than the training dataset.
  arXiv  Detail & Related papers  (2025-06-27T21:23:37Z)
• Action Flow Matching for Continual Robot Learning [57.698553219660376]
  Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks.<n>We introduce a generative framework leveraging flow matching for online robot dynamics model alignment.<n>We find that by transforming the actions themselves rather than exploring with a misaligned model, the robot collects informative data more efficiently.
  arXiv  Detail & Related papers  (2025-04-25T16:26:15Z)
• Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning [4.902161835372679]
  We propose a novel framework for uncertainty-aware policy optimization with model-based exploratory planning.<n>In the policy optimization phase, we leverage an uncertainty-driven exploratory policy to actively collect diverse training samples.<n>Our approach offers flexibility and applicability to tasks with varying state/action spaces and reward structures.
  arXiv  Detail & Related papers  (2025-03-26T01:07:35Z)
• STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models [21.929902181609936]
  We propose a novel approach to integrate uncertainty-based active learning and LoRA. For the uncertainty gap, we introduce a dynamic uncertainty measurement that combines the uncertainty of the base model and the uncertainty of the full model. For poor model calibration, we incorporate the regularization method during LoRA training to keep the model from being over-confident.
  arXiv  Detail & Related papers  (2024-03-02T10:38:10Z)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Deep Active Learning with Noise Stability [24.54974925491753]
  Uncertainty estimation for unlabeled data is crucial to active learning. We propose a novel algorithm that leverages noise stability to estimate data uncertainty. Our method is generally applicable in various tasks, including computer vision, natural language processing, and structural data analysis.
  arXiv  Detail & Related papers  (2022-05-26T13:21:01Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)
• Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage [7.358477502214471]
  We propose two new active learning algorithms for the Gaussian process with uncertainties. We show that the proposed approach can incorporate the impact from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.
  arXiv  Detail & Related papers  (2020-04-23T02:04:53Z)

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.