Papers Imitation Learning
“Imitation Learning” 태그가 달린 논문 2,122편 · 필터 해제
The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Length generalization, the ability to solve problems of longer sequences than those observed during training, poses a core challenge of Transformer-based large language models (LLM). Although existing studies have predom…
Imitation LearningSupervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Behavior Cloning (BC) on curated (or filtered) data is the predominant paradigm for supervised fine-tuning (SFT) of large language models; as well as for imitation learning of control policies. Here, we draw on a connect…
continuous-controlContinuous ControlImitation LearningReinforcement Learning (RL)EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow
Current language-guided robotic manipulation systems often require low-level action-labeled datasets for imitation learning. While object-centric flow prediction methods mitigate this issue, they remain limited to scenar…
Deformable Object ManipulationImitation LearningObjectFast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model
In recent years, the advancement of imitation learning has led to increased interest in teleoperating low-cost manipulators to collect demonstration data. However, most existing systems rely on unilateral control, which …
Imitation LearningLeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases. Existing systems fail to effectively interpret semantic information within tra…
Autonomous DrivingImitation LearningLanguage ModelingLanguage Modelling+1Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been p…
Imitation LearningTrajectory PlanningWorld-aware Planning Narratives Enhance Large Vision-Language Model Planner
Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic im…
Imitation LearningLanguage ModelingLanguage ModellingSpatial Reasoning+1Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to learn the expert policy from a limited numb…
Imitation LearningMuJoCoArk: An Open-source Python-based Framework for Robot Learning
Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major b…
Imitation LearningMotion PlanningCodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity
Natural language instructions for robotic manipulation tasks often exhibit ambiguity and vagueness. For instance, the instruction "Hang a mug on the mug tree" may involve multiple valid actions if there are several mugs …
Action GenerationContact-rich ManipulationImitation LearningHuman2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way remains a significant challenge. In this…
Imitation LearningRobust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as…
HallucinationImitation LearningRobot ManipulationLearning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors
Animals flexibly recombine a finite set of core motor primitives to meet diverse task demands, but existing behavior-segmentation methods oversimplify this process by imposing discrete syllables under restrictive generat…
Imitation LearningRepresentation LearningSteering Robots with Inference-Time Interactions
Imitation learning has driven the development of generalist policies capable of autonomously solving multiple tasks. However, when a pretrained policy makes errors during deployment, there are limited mechanisms for user…
Imitation LearningA Survey on Imitation Learning for Contact-Rich Tasks in Robotics
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge…
Contact-rich ManipulationImitation LearningSensitivityTouch begins where vision ends: Generalizable policies for contact-rich manipulation
Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generalizable policies. We introduce VisuoTactil…
Contact-rich ManipulationImitation LearningWhat Matters in Learning from Large-Scale Datasets for Robot Manipulation
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent on building such large-scale datasets aro…
DiversityImitation LearningRetrievalRobot ManipulationAdapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence
End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act …
Imitation LearningSAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to executing the task at the same speed …
Imitation LearningSchedulingHuman-Robot Navigation using Event-based Cameras and Reinforcement Learning
This work introduces a robot navigation controller that combines event cameras and other sensors with reinforcement learning to enable real-time human-centered navigation and obstacle avoidance. Unlike conventional image…
Imitation LearningRobot Navigation