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Notes on robotics
36 notes, page 2 of 2
A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols
The paper presents ProtoPilot, a self-evolving multi-agent system designed to automate the generation and execution of biological protocols. The system achieved high success rates in various tasks compared to…
MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
The research paper introduces MECoBench, a benchmark for evaluating the collaboration abilities of multimodal agents in visually grounded environments. The study explores three key findings: collaboration generally…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers have developed a new framework called OmniAct for building embodied agents that can operate in unstructured environments. This framework aims to address the challenges of managing both cyber and physical…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The paper presents OmniAct, a framework designed to create persistent embodied agents that can operate autonomously in unstructured environments. It addresses the challenges of integrating cyber (APIs, IoT) and…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers present OmniAct, a framework that aims to advance the development of persistent embodied agents in unstructured environments. The framework integrates a multimodal semantic planner for skill routing, an…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. This method uses vision language models (VLMs) to learn the potential function…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method for reinforcement learning agents using vision language models (VLMs). The approach called VLM-guided PBRS learns a potential function from VLM feedback to address the challenge…
Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
Researchers have developed a new camera planning framework called Look-Before-Move for embodied AI operating in dynamic 3D environments. The goal is to enable the camera to actively decide what to observe, not just…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper introduces Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method for inferring which agents are working together and what each…
NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
Researchers have developed a transformer-based multimodal framework named NoContactNoWorries that allows robots to infer binary contact states from RGB-D vision and the robot's proprioception. This approach offers a…
Project Fetch: Phase Two
Anthropic's Frontier Red Team ran a follow-up to their August 2025 "Project Fetch" robotics experiment. Using Claude Opus 4.7 with adaptive thinking (maximum effort) and Claude Code, they measured how fast the model…
Project Fetch: Phase Two
A follow-up to "Project Fetch" (August 2025) in which Anthropic's Frontier Red Team ran Claude Opus 4.7 autonomously on the same robotics tasks previously assigned to human-AI hybrid teams. The model completed those…
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