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Found 30 papers, 5 papers with code
PACER: Preference-conditioned All-terrain Costmap Generation
In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost.
STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
Terrain awareness, i. e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation.
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning
In this work, we posit that operator preferences for visually novel terrains, which the robot should adhere to, can often be extrapolated from established terrain references within the inertial, proprioceptive, and tactile domain.
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning
Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indicated by this data.
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning
While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so typically assume that the requisite behavior demonstrations are provided by an expert that behaves optimally with respect to a task reward.
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics
In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future.
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans.
Adversarial Imitation Learning from Video using a State Observer
The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone.
Visual Representation Learning for Preference-Aware Path Planning
We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space.
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making.
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation
Is imitation learning for vision based autonomous navigation even possible in such scenarios?
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning
While Adversarial Imitation Learning (AIL) algorithms have recently led to state-of-the-art results on various imitation learning benchmarks, it is unclear as to what impact various design decisions have on performance.
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
Learning from demonstrations in the wild (e. g. YouTube videos) is a tantalizing goal in imitation learning.
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
In this work, we introduce Lucid Dreaming for Experience Replay (LiDER), a conceptually new framework that allows replay experiences to be refreshed by leveraging the agent's current policy.
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning.
APPLD: Adaptive Planner Parameter Learning from Demonstration
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner.
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge.
Sample-efficient Adversarial Imitation Learning from Observation
We test our algorithm and conduct experiments using an imitation task on a physical robot arm and its simulated version in Gazebo and will show the improvement in learning rate and efficiency.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch.
Recent Advances in Imitation Learning from Observation
Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task.
Imitation Learning from Video by Leveraging Proprioception
Classically, imitation learning algorithms have been developed for idealized situations, e. g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions.
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}.
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance.
Generative Adversarial Imitation from Observation
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions.
Behavioral Cloning from Observation
In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects.
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data.
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage they require.
Decentralized Dynamic Discriminative Dictionary Learning
We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn a common set of dictionary elements of a feature space and model parameters while sequentially receiving observations.
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information.
