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Found 15 papers, 6 papers with code
Are Large Language Models Aligned with People's Social Intuitions for Human-Robot Interactions?
We find that GPT-4 strongly outperforms other models, generating answers that correlate strongly with users' answers in two studies $\unicode{x2014}$ the first study dealing with selecting the most appropriate communicative act for a robot in various situations ($r_s$ = 0. 82), and the second with judging the desirability, intentionality, and surprisingness of behavior ($r_s$ = 0. 83).
Cross Domain Policy Transfer with Effect Cycle-Consistency
Unlike prior methods that rely on paired data, we propose a novel approach for learning the mapping functions between state and action spaces across domains using unpaired data.
Learning to Solve Tasks with Exploring Prior Behaviours
Our method can endow agents with the ability to explore and acquire the required prior behaviours and then connect to the task-specific behaviours in the demonstration to solve sparse-reward tasks without requiring additional demonstration of the prior behaviours.
A Simple Recipe to Meta-Learn Forward and Backward Transfer
Meta-learning holds the potential to provide a general and explicit solution to tackle interference and forgetting in continual learning.
Neural Weight Search for Scalable Task Incremental Learning
Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting.
Policy Gradient With Serial Markov Chain Reasoning
We model agent behavior as the steady-state distribution of a parameterized reasoning Markov chain (RMC), optimized with a new tractable estimate of the policy gradient.
Automatic Context-Driven Inference of Engagement in HMI: A Survey
An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection.
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable.
GROWL: Group Detection With Link Prediction
Our proposed method, GROup detection With Link prediction (GROWL), demonstrates the effectiveness of a GNN based approach.
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks
Along with verbal communication, successful social interaction is closely coupled with the interplay between nonverbal perception and action mechanisms, such as observation of gaze behaviour and following their attention, coordinating the form and function of hand gestures.
Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning
Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns.
Learning Routines for Effective Off-Policy Reinforcement Learning
Within our framework, agents learn effective behavior over a routine space: a new, higher-level action space, where each routine represents a set of 'equivalent' sequences of granular actions with arbitrary length.
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay
However, finding a trade-off between the model performance and the number of samples to save for each class is still an open problem for replay-based incremental learning and is increasingly desirable for real-life applications.
Domain-Robust Visual Imitation Learning with Mutual Information Constraints
Human beings are able to understand objectives and learn by simply observing others perform a task.
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based on sparse local features and hypergraph matching.
