Tootfinder

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Shunpeng Yang, Zhen Fu, Zhefeng Cao, Guo Junde, Patrick Wensing, Wei Zhang, Hua Chen
https://arxiv.org/abs/2506.11470

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Generalizing locomotion policies across diverse legged robots with varying morphologies is a key challenge due to differences in observation/action dimensions and system dynamics. In this work, we propose Multi-Loco, a novel unified framework combining a morphology-agnostic generative diffusion model with a lightweight residual policy optimized via reinforcement learning (RL). The diffusion model captures morphology-invariant locomotion patterns from diverse cross-embodiment datasets, improving…

@… once you understand the dissonance of https://www.rollingstone.com/politics/poli…

"I am an embodiment journeying towards belief. Faith seeking greater confidence. That is what being a Christian means to me. It may well be the work of a lifetime."
—Ian Barclay ’24 M.A.R. writing in the new issue of our journal, Reflections https://…

To Embody or Not: The Effect Of Embodiment On User Perception Of LLM-based Conversational Agents
Kyra Wang, Boon-Kiat Quek, Jessica Goh, Dorien Herremans
https://arxiv.org/abs/2506.02514

To Embody or Not: The Effect Of Embodiment On User Perception Of LLM-based Conversational Agents
Embodiment in conversational agents (CAs) refers to the physical or visual representation of these agents, which can significantly influence user perception and interaction. Limited work has been done examining the effect of embodiment on the perception of CAs utilizing modern large language models (LLMs) in non-hierarchical cooperative tasks, a common use case of CAs as more powerful models become widely available for general use. To bridge this research gap, we conducted a mixed-methods withi…

america-chan

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Li Kang, Xiufeng Song, Heng Zhou, Yiran Qin, Jie Yang, Xiaohong Liu, Philip Torr, Lei Bai, Zhenfei Yin
https://arxiv.org/abs/2506.09049

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Coordinating multiple embodied agents in dynamic environments remains a core challenge in artificial intelligence, requiring both perception-driven reasoning and scalable cooperation strategies. While recent works have leveraged large language models (LLMs) for multi-agent planning, a few have begun to explore vision-language models (VLMs) for visual reasoning. However, these VLM-based approaches remain limited in their support for diverse embodiment types. In this work, we introduce VIKI-Bench…

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
Hongyan Zhi, Peihao Chen, Siyuan Zhou, Yubo Dong, Quanxi Wu, Lei Han, Mingkui Tan
https://arxiv.org/abs/2506.06199

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
Manipulation has long been a challenging task for robots, while humans can effortlessly perform complex interactions with objects, such as hanging a cup on the mug rack. A key reason is the lack of a large and uniform dataset for teaching robots manipulation skills. Current robot datasets often record robot action in different action spaces within a simple scene. This hinders the robot to learn a unified and robust action representation for different robots within diverse scenes. Observing how …

Parece interessante. Viu essa, @… ?
@… https://

Philosophical Psychology (@PhilosophicalPsychology@fediscience.org)
Attached: 1 image Free access! Sean Smith reviews "Buddhist Philosophy and the Embodied Mind: A Constructive Engagement" (Rowan & Littlefield) by Matthew MacKenzie https://doi.org/10.1080/09515089.2022.2159798 #Buddhism #embodiment

I think we will soon see an AlphaGo moment somewhere in embodiment. Maybe in robot football?
pi_0 is the Atari moment: https://www.physicalintelligence.company/blog/pi0 We now know that training at scale works and generalizes remarkably well.
This is the trigge…

Our First Generalist Policy
Our first generalist policy, π0, a prototype model that combines large-scale multi-task and multi-robot data collection with a new network architecture to enable the most capable and dexterous generalist robot policy to date.

#SlamRadio - 646 - Daryl Stay
https://on.soundcloud.com/kzFNChYGxTHrNeS22E
> UK-based artist Daryl Stay is the living embodiment of Acid House. A fan of electronic music…

This https://arxiv.org/abs/2503.08570 has been replaced.
initial toot: https://mastoxiv.page/@a…

Kramers nodal lines in intercalated TaS$_2$ superconductors
Kramers degeneracy is one fundamental embodiment of the quantum mechanical nature of particles with half-integer spin under time reversal symmetry. Under the chiral and noncentrosymmetric achiral crystalline symmetries, Kramers degeneracy emerges respectively as topological quasiparticles of Weyl fermions and Kramers nodal lines (KNLs), anchoring the Berry phase-related physics of electrons. However, an experimental demonstration for ideal KNLs well isolated at the Fermi level is lacking. Here,…

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, Raunaq Bhirangi, Siddhant Haldar, Pieter Abbeel, Lerrel Pinto
https://arxiv.org/abs/2506.01944

Feel the Force: Contact-Driven Learning from Humans
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contac…

DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
Zhao Mandi, Yifan Hou, Dieter Fox, Yashraj Narang, Ajay Mandlekar, Shuran Song
https://arxiv.org/abs/2505.24853

DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
We study the problem of functional retargeting: learning dexterous manipulation policies to track object states from human hand-object demonstrations. We focus on long-horizon, bimanual tasks with articulated objects, which is challenging due to large action space, spatiotemporal discontinuities, and embodiment gap between human and robot hands. We propose DexMachina, a novel curriculum-based algorithm: the key idea is to use virtual object controllers with decaying strength: an object is first…