1 citations · 1 across the 10 of their papers we have counts for
10 papers
A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies
Mathilde Kappel, Clémence Grislain, Mohamed Chetouani +5
Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioce…
PRISM: Perception Reasoning Interleaved for Sequential Decision Making
Mohamed Salim Aissi, Clemence Grislain, Clement Romac +4
Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remains a major challenge. Recent work identifies a perception-reasoning-decision gap i…
IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models
Hamed Rahimi, Clemence Grislain, Adrien Jacquet Cretides +2
Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly…
Online Self-Training for Co-Adaptation in Hierarchical Diffusion Policies
Clemence Grislain, Mathilde Kappel, Olivier Sigaud +1
Hierarchical policies decompose language-conditioned long-horizon robotic manipulation into a high-level planner and a low-level controller. However, effective coordination between…
Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Wenli Shi, Clemence Grislain, Olivier Sigaud +1
Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods…
I-FailSense: Towards General Robotic Failure Detection with Vision-Language Models
Clemence Grislain, Hamed Rahimi, Olivier Sigaud +1
Language-conditioned robotic manipulation in open-world settings requires not only accurate task execution but also the ability to detect failures for robust deployment in real-wor…
VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making
Mohamed Salim Aissi, Clemence Grislain, Mohamed Chetouani +3
While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruc…
IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving
Clémence Grislain, Risto Vuorio, Cong Lu +1
Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achiev…
A Bayesian Solution To The Imitation Gap
Risto Vuorio, Mattie Fellows, Cong Lu +2
In many real-world settings, an agent must learn to act in environments where no reward signal can be specified, but a set of expert demonstrations is available. Imitation learning…
Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind
Clémence Grislain, Hugo Caselles-Dupré, Olivier Sigaud +1
Good teachers always tailor their explanations to the learners. Cognitive scientists model this process under the rationality principle: teachers try to maximise the learner's util…