arXiv Machine Learning
MA-UITD: Offline Multi-Agent Reward Inference for Characterizing Continuous Social Behavior
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arXiv AI
Jul 10
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
arXiv:2607. 08647v1 Announce Type: cross Abstract: As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment.
arXiv Machine Learning
Jun 25
Consistent Zero-Shot Imitation with Contrastive Goal Inference
arXiv:2510. 17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.
arXiv Machine Learning
Jun 18
Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents
arXiv:2606. 18537v1 Announce Type: new Abstract: Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act in an environment.
arXiv AI
Jun 19
Sensorimotor World Models: Perception for Action via Inverse Dynamics
arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.
arXiv AI
6d ago
Learning Intrinsic Dynamics in Latent World Models
The paper investigates how encouraging action predictability in latent world models can preserve intrinsic dynamics, which are crucial for planning. By deriving a theoretical lower bound and conducting empirical tests, the authors show that action predictability prevents the encoder from merging distinct action outcomes, even when visual appearance varies. Using a method called Concat IDM, they achieve a substantial increase in planning success on the Push‑T benchmark, from 9.1% to 77.2%, demonstrating the practical benefit of this inductive bias.
arXiv AI
Jun 11
Implicit Neural Representations of Individual Behavior
arXiv:2606. 12200v1 Announce Type: cross Abstract: We study policy representation learning from unlabeled multi-policy behavioral data.