Robotics & Embodied AI

Physical AI, robots, and autonomous systems

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Llms

An attack class that passes every current LLM filter - no payload, no injection signature, no log trace

https://shapingrooms.com/research I published a paper today on something I've been calling postural manipulation. The short version: ordi...

Reddit - Artificial Intelligence · 1 min ·
Llms

[R] An attack class that passes every current LLM filter - no payload, no injection signature, no log trace

https://shapingrooms.com/research I've been documenting what I'm calling postural manipulation: a specific class of language that install...

Reddit - Machine Learning · 1 min ·
[2601.07855] RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution
Machine Learning

[2601.07855] RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution

Abstract page for arXiv paper 2601.07855: RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution

arXiv - AI · 3 min ·

All Content

[2507.16874] Budget Allocation Policies for Real-Time Multi-Agent Path Finding
Robotics

[2507.16874] Budget Allocation Policies for Real-Time Multi-Agent Path Finding

This article presents a study on budget allocation policies for real-time multi-agent path finding (RT-MAPF), focusing on improving effic...

arXiv - AI · 4 min ·
[2507.10846] Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization
Machine Learning

[2507.10846] Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization

Winsor-CAM introduces a novel method for visual explanations in deep networks, enhancing interpretability through human-tunable parameter...

arXiv - Machine Learning · 4 min ·
[2505.20181] The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected World
Robotics

[2505.20181] The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected World

The paper discusses the systemic risks posed by algorithmic collisions in interconnected AI systems, highlighting the need for improved g...

arXiv - AI · 4 min ·
[2505.16547] Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation
Machine Learning

[2505.16547] Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation

This paper presents a zero-shot reinforcement learning framework for occlusion-aware plant manipulation, achieving high success rates in ...

arXiv - AI · 3 min ·
[2503.14637] KINESIS: Motion Imitation for Human Musculoskeletal Locomotion
Machine Learning

[2503.14637] KINESIS: Motion Imitation for Human Musculoskeletal Locomotion

KINESIS presents a model-free framework for motion imitation in human musculoskeletal locomotion, achieving robust performance in various...

arXiv - Machine Learning · 4 min ·
[2512.00672] ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
Llms

[2512.00672] ML-Tool-Bench: Tool-Augmented Planning for ML Tasks

The paper presents ML-Tool-Bench, a benchmark for evaluating tool-augmented planning in machine learning tasks, addressing the limitation...

arXiv - AI · 4 min ·
[2412.04272] PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables
Llms

[2412.04272] PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables

The paper presents PoTable, a novel approach to table reasoning that integrates systematic thinking through a plan-then-execute mechanism...

arXiv - AI · 4 min ·
[2511.12779] Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation
Llms

[2511.12779] Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation

This paper presents a novel approach to multi-objective reinforcement learning by introducing a two-stage procedure that efficiently esti...

arXiv - AI · 4 min ·
[2511.07730] Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning
Nlp

[2511.07730] Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning

This paper presents a novel approach to goal-conditioned reinforcement learning (GCRL) using multistep quasimetric learning, demonstratin...

arXiv - Machine Learning · 4 min ·
[2204.07520] Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
Robotics

[2204.07520] Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

This paper presents a novel algorithm for resource-aware distributed submodular maximization, enhancing multi-robot decision-making by ba...

arXiv - AI · 4 min ·
[2509.20648] Wonder Wins Ways: Curiosity-Driven Exploration through Multi-Agent Contextual Calibration
Robotics

[2509.20648] Wonder Wins Ways: Curiosity-Driven Exploration through Multi-Agent Contextual Calibration

This paper presents CERMIC, a novel framework for enhancing multi-agent exploration in reinforcement learning by calibrating intrinsic cu...

arXiv - Machine Learning · 4 min ·
[2512.20798] A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents
Robotics

[2512.20798] A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents

This paper introduces a benchmark for evaluating outcome-driven constraint violations in autonomous AI agents, highlighting safety concer...

arXiv - AI · 4 min ·
[2502.08047] WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point
Ai Agents

[2502.08047] WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point

WorldGUI introduces a benchmark for evaluating desktop GUI automation agents under varied initial conditions, addressing the challenges o...

arXiv - AI · 4 min ·
[2404.09877] Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
Machine Learning

[2404.09877] Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

This paper presents a novel cognitive architecture that combines human-like responses with machine intelligence for effective disaster re...

arXiv - AI · 4 min ·
[2602.20144] Agentic AI for Scalable and Robust Optical Systems Control
Machine Learning

[2602.20144] Agentic AI for Scalable and Robust Optical Systems Control

The paper presents AgentOptics, an AI framework for autonomous control of optical systems, achieving high task success rates and demonstr...

arXiv - AI · 4 min ·
[2503.23608] Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's
Machine Learning

[2503.23608] Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's

This paper explores a high-dimensional computing architecture that mimics biological learning processes, proposing a model that integrate...

arXiv - Machine Learning · 4 min ·
[2602.20119] NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning
Llms

[2602.20119] NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning

NovaPlan introduces a framework for zero-shot long-horizon manipulation in robotics, integrating video language planning with geometrical...

arXiv - AI · 4 min ·
[2602.20076] Robust Taylor-Lagrange Control for Safety-Critical Systems
Ai Safety

[2602.20076] Robust Taylor-Lagrange Control for Safety-Critical Systems

The paper presents a robust Taylor-Lagrange Control (rTLC) method for safety-critical systems, addressing the feasibility preservation pr...

arXiv - AI · 3 min ·
[2602.20057] AdaWorldPolicy: World-Model-Driven Diffusion Policy with Online Adaptive Learning for Robotic Manipulation
Machine Learning

[2602.20057] AdaWorldPolicy: World-Model-Driven Diffusion Policy with Online Adaptive Learning for Robotic Manipulation

The paper presents AdaWorldPolicy, a novel framework for robotic manipulation that utilizes world models and online adaptive learning to ...

arXiv - AI · 4 min ·
[2602.20055] To Move or Not to Move: Constraint-based Planning Enables Zero-Shot Generalization for Interactive Navigation
Robotics

[2602.20055] To Move or Not to Move: Constraint-based Planning Enables Zero-Shot Generalization for Interactive Navigation

This paper presents a novel constraint-based planning framework for mobile robots, enabling zero-shot generalization in interactive navig...

arXiv - AI · 4 min ·
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