Papers Behavioural cloning
“Behavioural cloning” 태그가 달린 논문 45편 · 필터 해제
What Do You Need for Diverse Trajectory Stitching in Diffusion Planning?
In planning, stitching is an ability of algorithms to piece together sub-trajectories of data they are trained on to generate new and diverse behaviours. While stitching is historically a strength of offline reinforcemen…
Behavioural cloningData AugmentationEvaluation-Time Policy Switching for Offline Reinforcement Learning
Offline reinforcement learning (RL) looks at learning how to optimally solve tasks using a fixed dataset of interactions from the environment. Many off-policy algorithms developed for online learning struggle in the offl…
Behavioural cloningOffline RLreinforcement-learningReinforcement Learning+1Problem Space Transformations for Out-of-Distribution Generalisation in Behavioural Cloning
The combination of behavioural cloning and neural networks has driven significant progress in robotic manipulation. As these algorithms may require a large number of demonstrations for each task of interest, they remain …
Behavioural cloningImproving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales
When interacting with each other, humans adjust their behavior based on perceived trust. However, to achieve similar adaptability, robots must accurately estimate human trust at sufficiently granular timescales during th…
Bayesian InferenceBehavioural cloningHuman-Object Relationship DetectionRobot ManipulationOffline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations
Offline-to-online reinforcement learning (O2O RL) aims to obtain a continually improving policy as it interacts with the environment, while ensuring the initial policy behaviour is satisficing. This satisficing behaviour…
Behavioural cloningVision-Language Navigation with Energy-Based Policy
Vision-language navigation (VLN) requires an agent to execute actions following human instructions. Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manua…
Behavioural cloningVision-Language NavigationAutonomous Vehicle Controllers From End-to-End Differentiable Simulation
Current methods to learn controllers for autonomous vehicles (AVs) focus on behavioural cloning. Being trained only on exact historic data, the resulting agents often generalize poorly to novel scenarios. Simulators prov…
Autonomous VehiclesBehavioural cloningSocial Learning through Interactions with Other Agents: A Survey
Social learning plays an important role in the development of human intelligence. As children, we imitate our parents' speech patterns until we are able to produce sounds; we learn from them praising us and scolding us; …
Behavioural cloningSurveyExplorative Imitation Learning: A Path Signature Approach for Continuous Environments
Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to increase generalisation and human interv…
Behavioural cloningImitation LearningEvIL: Evolution Strategies for Generalisable Imitation Learning
Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation…
Behavioural cloningcontinuous-controlContinuous ControlImitation LearningClosing the gap: Optimizing Guidance and Control Networks through Neural ODEs
We improve the accuracy of Guidance & Control Networks (G&CNETs), trained to represent the optimal control policies of a time-optimal transfer and a mass-optimal landing, respectively. In both cases we leverage the dynam…
Behavioural cloningPolicy Improvement using Language Feedback Models
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following. To train LFMs, we obtain fe…
Behavioural cloningImitation LearningInstruction FollowingOIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learn…
Anomaly DetectionBehavioural cloningDecision MakingImitation Learning+2Robust Imitation Learning for Automated Game Testing
Game development is a long process that involves many stages before a product is ready for the market. Human play testing is among the most time consuming, as testers are required to repeatedly perform tasks in the searc…
Behavioural cloningImitation LearningNavigateBehavioural Cloning in VizDoom
This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) using only pixel data as input. We also explore how Reinforcement Learning (RL) compares to IL for hum…
Behavioural cloningImitation LearningReinforcement Learning (RL)On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end behavioural cloning methods. In this paper, we explore an alternative paradigm which decomposes reasoning into three p…
Behavioural cloningImitation LearningObjectRetrievalWorking Backwards: Learning to Place by Picking
We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, contact-constrained locations. With PvP, we…
Behavioural cloningRObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks
Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area o…
Behavioural cloningImitation LearningBehavioral Cloning via Search in Embedded Demonstration Dataset
Behavioural cloning uses a dataset of demonstrations to learn a behavioural policy. To overcome various learning and policy adaptation problems, we propose to use latent space to index a demonstration dataset, instantly …
Behavioural cloningMinecraftSelf-Supervised Adversarial Imitation Learning
Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unlabelled snapshots of the states to decod…
Behavioural cloningImitation Learning