imitation-learning

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.

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.

Introduces digital teleoperation using action-conditioned world models to generate diverse training data for robotics, decoupling data collection from physical hardware. The system achieves real-time generation and enables zero-shot Sim2Real transfer.

SIEVE is a structure-aware data selection method for vision-language-action imitation learning that identifies reusable visuo-motor primitives and transition interfaces, outperforming full-data training with only 50% of demonstrations and training steps.

Introduces Φ-Nav, a unified on-policy framework that uses hindsight reasoning to synthetically generate path-level instructions from exploratory trajectories, bridging the semantic supervision gap in Vision-Language Navigation and achieving competitive results on R2R-CE and RxR-CE benchmarks with fewer expert demonstrations.

Active-GRPO introduces an adaptive imitation and self-improving reasoning framework that dynamically decides when to imitate references and when to reinforce the model's own discoveries for molecular optimization, achieving statistically significant improvements over previous methods on the TOMG-Bench-MolOpt benchmark.

This paper proposes a noisy expert model to explain the gap between offline and online imitation learning, showing that offline learning from noisy trajectories requires exponential sample complexity while online on-policy distillation achieves polynomial dependence. The analysis leads to an alternative loss function and experiments confirm the theoretical findings.

ReGuide introduces a self-improving framework for diffusion policies that uses test-time guidance to generate corrective rollouts, then fine-tunes the policy on this data, achieving 1.3–7.7× success improvement on Robomimic tasks.

Lerobot SO-101 is a framework that transforms robot learning from messy lab magic into a clean, reproducible software pipeline, enabling structured data collection, policy training, and deployment, similar to what PyTorch did for deep learning.

This paper introduces GRID, a social learning method that extracts universal behaviors from heterogeneous agents by decomposing per-agent reward functions into general and specific rewards, enabling a generalist agent that avoids mode-averaging bias.

This paper investigates whether per-phase metric selection improves demonstration curation for behavior cloning policies. The authors find that phase-gated curation never outperforms global or uniform metric application, and the dilution of defect signals across phases explains the failure.

RL4IL introduces a reinforcement learning-guided retrieval method that uses soft fusion over frozen demonstration libraries to handle missing sensor modalities in robotic imitation learning at inference time, achieving high success rates under complete camera dropout.