arXiv subjects

Yinsen Jia

Publications and source records attributed to Yinsen Jia.

5 recordsLinked to original sources

arXiv2026

Temporal Self-Imitation Learning

Long-horizon policies trained with reinforcement learning can still achieve high return through inefficient interactions, while rare efficient behaviors discovered during training may be forgotten. We argue that temporal efficiency itself provides a source of self-supervision for reinforcement learning. We introduce Temporal Self-Imitation Learning (TSIL), a reinforcement learning framework that mines temporally efficient successful trajectories generated during learning and converts them into reusable supervision for future policy improvement. TSIL progressively refines learning using configuration-conditioned adaptive temporal targets derived from fast successful trajectories, while preserving and replaying efficient behaviors through efficiency-weighted self-imitation learning. Across 30 long-horizon tasks spanning robot manipulation and interactive navigation, TSIL consistently improves task success rates, learning efficiency, behavioral efficiency, and robustness to unstable training conditions.

cs.RO↗

arXiv2026

Time as a Control Dimension in Robot Learning

Temporal awareness plays a central role in intelligent behavior by shaping how actions are paced, coordinated, and adapted to changing goals and environments. In contrast, most robot learning algorithms treat time only as a fixed episode horizon or scheduling constraint. Here we introduce time-aware policy learning, a reinforcement learning framework that treats time as a control dimension of robot behavior. The approach augments policies with two temporal signals, the remaining time and a time ratio that modulates the policy's internal progression of time, allowing a single policy to regulate its execution strategy across temporal regimes. Across diverse manipulation tasks including long-horizon manipulation, granular-media pouring, articulated-object interaction, and multi-agent coordination, the resulting policies adapt their behavior continuously from dynamic execution under tight schedules to stable and deliberate interaction when more time is available. This temporal awareness improves efficiency, robustness under sim-to-real mismatch and disturbances, and controllability through human input without retraining. Treating time as a controllable variable provides a new framework for adaptive and human-aligned robot autonomy.

cs.RO↗

arXiv2025

Tactile-based Object Retrieval From Granular Media

We introduce GEOTACT, the first robotic system capable of grasping and retrieving objects of potentially unknown shapes buried in a granular environment. While important in many applications, ranging from mining and exploration to search and rescue, this type of interaction with granular media is difficult due to the uncertainty stemming from visual occlusion and noisy contact signals. To address these challenges, we use a learning method relying exclusively on touch feedback, trained end-to-end with simulated sensor noise. We show that our problem formulation leads to the natural emergence of learned pushing behaviors that the manipulator uses to reduce uncertainty and funnel the object to a stable grasp despite spurious and noisy tactile readings. We introduce a training curriculum that bootstraps learning in simulated granular environments, enabling zero-shot transfer to real hardware. Despite being trained only on seven objects with primitive shapes, our method is shown to successfully retrieve 35 different objects, including rigid, deformable, and articulated objects with complex shapes. Videos and additional information can be found at https://jxu.ai/geotact.

cs.RO↗

arXiv2024

ClutterGen: A Cluttered Scene Generator for Robot Learning

We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and collision. As the number of objects increases, finding valid poses becomes more difficult, necessitating significant human engineering effort, which limits the diversity of the scenes. To overcome these challenges, we propose a reinforcement learning method that can be trained with physics-based reward signals provided by the simulator. Our experiments demonstrate that ClutterGen can generate cluttered object layouts with up to ten objects on confined table surfaces. Additionally, our policy design explicitly encourages the diversity of the generated scenes for open-ended generation. Our real-world robot results show that ClutterGen can be directly used for clutter rearrangement and stable placement policy training.

cs.RO↗

arXiv2024

Dynamic Grasping with a Learned Meta-Controller

Grasping moving objects is a challenging task that requires multiple submodules such as object pose predictor, arm motion planner, etc. Each submodule operates under its own set of meta-parameters. For example, how far the pose predictor should look into the future (i.e., look-ahead time) and the maximum amount of time the motion planner can spend planning a motion (i.e., time budget). Many previous works assign fixed values to these parameters; however, at different moments within a single episode of dynamic grasping, the optimal values should vary depending on the current scene. In this work, we propose a dynamic grasping pipeline with a meta-controller that controls the look-ahead time and time budget dynamically. We learn the meta-controller through reinforcement learning with a sparse reward. Our experiments show the meta-controller improves the grasping success rate (up to 28% in the most cluttered environment) and reduces grasping time, compared to the strongest baseline. Our meta-controller learns to reason about the reachable workspace and maintain the predicted pose within the reachable region. In addition, it assigns a small but sufficient time budget for the motion planner. Our method can handle different objects, trajectories, and obstacles. Despite being trained only with 3-6 random cuboidal obstacles, our meta-controller generalizes well to 7-9 obstacles and more realistic out-of-domain household setups with unseen obstacle shapes.

cs.RO↗