arXiv AI
Representing short-term movement to reveal animal identity and learning progress
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arXiv AI
Oct 16
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 Computer Vision
Sep 15
Reconstructing Is Not Acting: Action-Centric Latent Dynamics Modeling
arXiv:2609.15189v1 Announce Type: new Abstract: Latent action models (LAMs) learn action representations from unlabeled videos by inferring latent actions from visual transitions and reconstructing f...
arXiv Machine Learning
Oct 8
MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
MorphCL introduces a self‑supervised pretraining framework for inertial‑based human activity recognition that explicitly models global motion structure through morphology‑aware grouping. By leveraging motion primitives and domain‑specific descriptors, MorphCL improves linear probing and fine‑tuning performance of encoders by up to 15 percentage points in F1‑score. Compared with existing foundation models, MorphCL‑pretrained encoders achieve comparable or better linear probing results while using 4,600× less data, and the learned embeddings exhibit clear, morphologically meaningful clusters that better separate kinematically similar activities.
arXiv AI
Jul 16
Classifying daily activities needs posture, reconstructing them needs motion
arXiv:2607. 13216v1 Announce Type: cross Abstract: Humans recognize movements effortlessly, even from noisy and complex visual input.
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.
arXiv AI
Sep 10
RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts
RevalExo is a new benchmark for locomotion mode recognition that focuses on functional daily activities performed by older adults and clinical cohorts. It includes 27 participants from three groups—healthy older adults, stroke survivors, and older adults with probable sarcopenia—recorded with lower-body IMUs and, for a subset, synchronized egocentric video. The dataset offers 10.1 hours of frame‑level annotations across 11 locomotion modes, and the authors evaluate unimodal, multimodal, cross‑population, and cross‑modal recognition challenges, finding that sensor fusion improves performance but transitions and generalization remain difficult.