arXiv:2606. 03512v1 Announce Type: cross Abstract: Path planning is essential for Autonomous Mobile Robots (AMRs).
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
arXiv:2606. 10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC).
arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.
arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.
EVOL is a framework that uses a knowledge‑tracing simulator to generate per‑learner expert demonstrations via evolutionary search, and then trains a deployment‑free policy that distills these demonstrations into a feed‑forward learner. The method employs an asymmetric actor‑critic architecture, where the actor plans blindly as in real deployment while the critic has access to privileged simulator state during training. Across three educational datasets and varying path lengths, EVOL outperforms eight baselines and shows that the quality of evolutionary experts, rather than the imitation objective, drives final performance.
ObstaDiff is a diffusion-policy framework that introduces a lightweight obstacle-aware visual encoder to generate structured representations of targets, obstacles, and background. By aligning these representations, the policy produces end-effector trajectories that focus on a target-centered bottleneck pose while accounting for surrounding obstacles. In real-robot greenhouse trials, ObstaDiff achieved a 75.41% task success rate and an 8.20% obstacle collision rate, outperforming existing imitation-learning baselines in cluttered agricultural settings.
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:2512. 16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community.
SAIL is a framework that transforms robot imitation learning into an iterative refinement problem, enabling test-time scaling of trajectory generation. It employs Monte Carlo Tree Search where each node represents a full trajectory and edges denote refinements, guided by an archive of successful trajectories, a vision‑language model for scoring, and step‑level feedback. Experiments on six manipulation tasks in simulation and real‑world settings show that higher test‑time compute consistently raises success rates, reaching up to 95% on complex tasks.
Steertuning is a system that lets robots learn new tasks in new environments from only five human demonstrations, completing the entire process in under five minutes. It first pre‑trains a 400‑million‑parameter diffusion policy on 100 hours of egocentric human data, then fine‑tunes the model for just 2 minutes using the demonstration most similar to the current environment. The approach achieves a 71 % success rate across 12 tasks in 12 distinct settings, from labs to kitchens, and the authors will release the dataset, model weights, code, and robot videos.
LeWAM is an end‑to‑end joint‑embedding predictive architecture that learns compact latent world models without pixel reconstruction and directly supports policy execution and planning. It trains jointly on next‑latent‑state prediction and action‑flow matching while imposing an isotropic Gaussian constraint on the latent state. The approach yields significant performance gains, raising visual‑planning success rates from 28.6 % to 89.7 % on contact‑rich tasks, from 10.9 % to 46.2 % on long‑horizon tasks, and speeding up planning by 32.3×, while remaining competitive on behavior‑cloning tasks and providing interpretable control‑relevant representations.
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.