Imitation Learning
525 papers with code • 0 benchmarks • 18 datasets
Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.
Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning
Source: Learning to Imitate
Benchmarks
These leaderboards are used to track progress in Imitation Learning
Libraries
Use these libraries to find Imitation Learning models and implementationsDatasets
CARLA
AirSim
AI2-THOR
TORCS
RLBench
INTERACTION Dataset
ManiSkill2
CoDraw
CHALET
CubiCasa5K
Latest papers with no code
What AIs are not Learning (and Why): Bio-Inspired Foundation Models for Robots
They do not lead to robots that know enough to be deployed widely in service applications.
Supervised Fine-Tuning as Inverse Reinforcement Learning
The prevailing approach to aligning Large Language Models (LLMs) typically relies on human or AI feedback and assumes access to specific types of preference datasets.
Visuo-Tactile Pretraining for Cable Plugging
Our results show that by pretraining with tactile information, the performance of a non-tactile agent can be significantly improved, reaching a level on par with visuo-tactile agents.
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
Our experiments in both simulated and real-world environments demonstrate that our approach achieves superior performance and robustness than IL or RL alone in navigating a quadrotor through a racing course using only visual information without explicit state estimation.
SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction.
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces.
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks.
Physics-informed Neural Motion Planning on Constraint Manifolds
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds.
Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Recent works on large-scale robotic data collection typically vary a wide range of environmental factors during data collection, such as object types and table textures.
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data.
