DemoStream is a Task and Motion Planning (TAMP) system that uses GPU‑accelerated batched sampling to generate high‑quality demonstrations for robotic manipulation. By optimizing demonstration quality and exploring many solutions in parallel, it improves behavior cloning success rates and enables real‑world demo collection at an average of 66 demos per hour. The approach evaluates different action spaces, cost functions, and batch sizes in simulation to understand their impact on downstream policy performance.
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.
arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.
The paper introduces Success Guided Sampling (SGS), an adaptive sampler that focuses reinforcement learning on task configurations near the policy’s current frontier, thereby reducing wasted experience in massively parallel simulation. SGS enables large‑scale RL to solve complex quadruped locomotion and contact‑rich assembly tasks that previous methods could not, using up to one million parallel environments. The authors also distill the learned policies into RGB‑based models that transfer zero‑shot to real‑world assembly tasks.
arXiv:2609.36915v1 Announce Type: cross Abstract: Aerial manipulators extend robotic manipulation into 3D workspaces that are difficult for ground-based robots to access, creating new opportunities f...
Dex-One2Many introduces a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. By abstracting the video into sequential scene graphs, the method guides reinforcement learning with dense rewards and diverse reset states, enabling efficient exploration while preserving broad generalizability. Trained entirely in simulation, Dex-One2Many achieves zero‑shot transfer to a real multi‑fingered hand and outperforms baselines by 6.5% on seen configurations and 71% on unseen scenarios across five tool‑use and manipulation tasks.
The paper introduces Sampling-Guided Policy Search (SGPS), a method that combines sampling-based model‑predictive control with first‑order policy gradients to accelerate visual policy learning for locomotion and manipulation tasks. SGPS starts with behavior cloning from sampled actions and then alternates between sampling‑based refinement and short‑horizon policy updates under varied initial states and dynamics. The approach is demonstrated on simulated Unitree Go2 and G1 robots, learning tasks such as obstacle traversal and bimanual carrying, and the distilled policies transfer zero‑shot to a real Go2 robot using onboard depth perception.
arXiv:2608. 01452v1 Announce Type: cross Abstract: Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments.
arXiv:2608. 09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds.
arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.
arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection.
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.