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Found 28 papers, 10 papers with code
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency
Using ViSaRL to learn visual representations significantly improves the success rate, sample efficiency, and generalization of an RL agent on diverse tasks including DeepMind Control benchmark, robot manipulation in simulation and on a real robot.
A Generalized Acquisition Function for Preference-based Reward Learning
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task.
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.
Batch Active Learning of Reward Functions from Human Preferences
Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time.
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Active Reward Learning from Online Preferences
Robot policies need to adapt to human preferences and/or new environments.
Assistive Teaching of Motor Control Tasks to Humans
In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft.
Learning Preferences for Interactive Autonomy
To this end, we first propose various forms of comparative feedback, e. g., pairwise comparisons, best-of-many choices, rankings, scaled comparisons; and describe how a robot can use these various forms of human feedback to infer a reward function, which may be parametric or non-parametric.
Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction
At a certain time, to forecast a reasonable future trajectory, each agent needs to pay attention to the interactions with only a small group of most relevant agents instead of unnecessarily paying attention to all the other agents.
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
Our results show that the proposed partner-aware strategy outperforms other known methods, and our human subject studies suggest humans prefer to collaborate with AI agents implementing our partner-aware strategy.
Learning Reward Functions from Scale Feedback
Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences.
Learning Multimodal Rewards from Rankings
However, expert feedback is often assumed to be drawn from an underlying unimodal reward function.
APReL: A Library for Active Preference-based Reward Learning Algorithms
Reward learning is a fundamental problem in human-robot interaction to have robots that operate in alignment with what their human user wants.
Emergent Prosociality in Multi-Agent Games Through Gifting
Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game.
Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy
Traffic congestion has large economic and social costs.
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway.
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
Multi-Agent Safe Planning with Gaussian Processes
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together.
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences
As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers.
Active Preference-Based Gaussian Process Regression for Reward Learning
Our results in simulations and a user study suggest that our approach can efficiently learn expressive reward functions for robotics tasks.
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
Robots can learn the right reward function by querying a human expert.
Batch Active Learning Using Determinantal Point Processes
While active learning methods attempt to tackle this issue by labeling only the data samples that give high information, they generally suffer from large computational costs and are impractical in settings where data can be collected in parallel.
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models
We propose a safe exploration algorithm for deterministic Markov Decision Processes with unknown transition models.
Batch Active Preference-Based Learning of Reward Functions
Data generation and labeling are usually an expensive part of learning for robotics.
