imitation-learning

The paper proposes Predictive Action Chunk Learning (PACL) for robot manipulation, which learns from mixed-quality deployment experiences using a predictive critic and diffusion actor to improve policy performance.

This paper characterizes the minimal recurrent behavioral memory required for imitating an expert under partial observability, using information-theoretic measures and experimental validation.

The paper investigates the closed-loop driving competence of a multimodal behavioral-cloning policy trained on offline expert demonstrations in the CARLA simulator, demonstrating effective autonomous driving without collisions and releasing all artifacts.

This paper presents a large-scale empirical study of offline reinforcement and imitation learning, analyzing over 160,000 training runs to understand the effects of hyperparameters and dataset properties, and introduces JumpStart, a resource suite for reliable policy-learning research.

This research explores combining harness evolution with model adaptation for AI agents, discovering that direct imitation from experts harms weaker models' performance and proposing an on-policy correction method to improve performance without breaking harness fit for enterprise tasks.

This paper compares the temporal robustness of expert and imitation-learned policies in dexterous manipulation tasks, finding that imitation-learned policies degrade more sharply with increased execution speeds, primarily due to insertion misalignments.

This paper proposes a Latent World Model (LWM) for robot navigation that predicts action-conditioned latent feature compatibility, enabling policy learning from unlabeled video data and reinforcement learning without additional environment interaction, outperforming existing methods.

EXIMO proposes an efficient algorithm for fine-tuning vision-language-action robot policies using a three-stage process: VLM-guided exploration, imitation learning, and residual reinforcement learning, showing improved sample-efficiency and performance.

SAGE is a learned SLO-aware adaptive retrieval policy for production RAG systems that dynamically selects the number of retrieved passages per query, improving SLO compliance and reducing latency/cost with minimal quality loss.

Sergey Levine highlights a new paper investigating why action chunking is so effective in modern large-scale imitation learning for robotics, breaking down the underlying reasons.

Proposes Gated Hindsight Distillation (GHD), a method that uses future screenshots as privileged information to recover correct reasoning during training of mobile GUI agents, improving task success on AndroidWorld and AndroidLab across two vision-language models.

Khushi shares her reading notes on Action Chunking Transformers and Diffusion Policy, explaining how action chunking with generative models like VAEs and diffusion improves imitation learning for robotics, and how they solve inference latency with decoupled planning and execution.

This paper proposes 'Mirror Learning', a framework for imitation learning from third-person observation that uses a fine-tuned video diffusion model for perspective transformation and an inverse dynamics model to synthesize pseudo first-person expert data, showing that this mirror data alone can train effective policies and improve behavior cloning.

HiFi-UMI introduces a portable data-production system for robot-free UMI data that achieves high trajectory accuracy using stereo-inertial SLAM and wide-angle cameras. Training manipulation policies on this data alone enables zero-shot deployment on real robots, matching or exceeding teleoperation baselines across several model families, and the authors open-source a 2,000-hour high-fidelity dataset.

The article discusses the potential benefits and challenges of using first-person video for robot learning, highlighting that while direct imitation is limited, the sequence of visual attention may transfer. It references LingBot-VLA 2.0 and calls for controlled evaluations to separate viewpoint effects from data volume.

DeepEpoch is a no-code desktop app that learns to play 2D games by watching users play via behavior cloning, with human-in-the-loop fine-tuning.

Latent actions are gaining traction in robotics as a way to learn from unlabeled video without action labels. Recent papers from DeepMind and FAIR demonstrate progress from controlled game environments to in-the-wild internet video, promising scalable training for imitation learning.

This paper proposes a unified imitation learning framework using Taylor Series Imitation Learning and distributionally robust adaptive control to address both policy-induced and uncertainty-induced distribution shifts, with a UAV case study demonstrating safety under uncertainty.

The article discusses the shift from reactive to prediction-based robot control, highlighted by the LingBot-VA 2.0 model which can keep up with fast-moving objects like an air-hockey puck and learn from few demonstrations.

This paper introduces Feedback Manipulation Regularization (FMR), an algorithm-agnostic method that uses evaluative feedback to improve alignment in imitation learning, achieving up to 98% reduction in misalignment in Safety Gymnasium environments.