arXiv AI
Jul 17
RoboTTT: Context Scaling for Robot Policies
arXiv:2607. 15275v1 Announce Type: cross Abstract: Recent robot foundation models operate with single-step or short-history visuomotor context.
SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM
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.
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arXiv AI
Jun 16
Training and Evaluating Diffusion Policies with Long Context Lengths
arXiv:2606. 16447v1 Announce Type: cross Abstract: Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations.
arXiv AI
Jun 19
RoboSSM: Scalable In-context Imitation Learning via State-Space Models
arXiv:2509. 19658v2 Announce Type: replace-cross Abstract: In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations.
arXiv AI
Jul 13
ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning
arXiv:2512. 16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community.
Hugging Face Trending Papers
Jul 6
Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized.
arXiv Machine Learning
Aug 4
DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration
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 AI
Jun 11
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning
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 AI
2d ago
Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning
arXiv:2604.16683v2 Announce Type: replace-cross Abstract: Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a...
arXiv Machine Learning
Jul 20
Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
arXiv:2607. 15880v1 Announce Type: cross Abstract: Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization.
arXiv AI
Jun 2
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.
arXiv AI
Jul 7
Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning
arXiv:2607. 04591v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation.
Hugging Face Trending Papers
Aug 24
Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation
Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verification difficult. Hi...