21 citations · 34 across the 13 of their papers we have counts for
12 papers · 1 filter
Screw Attention: Rigid-Body Algebra Inside a Transformer
Aly Magassouba
Learned manipulation policies rediscover from data the spatial relations that rigid-body mechanics supplies in closed form, which leaves them fragile to geometric change. We presen…
PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
Koyo Fujii, Luis Figueredo, Praminda Caleb-Solly +4
Accurately estimating object mass and friction is fundamental to reliable robotic manipulation. While interactive perception is powerful, most approaches rely on specialized hardwa…
DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
Oussama Zaim, Mélodie Daniel, Aly Magassouba +2
Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-world dynamics. We address this…
ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions
Kohio Deflesselle, Mélodie Daniel, Aly Magassouba +2
Autonomous control of double-Ackermann-steering robots is essential in agricultural applications, where robots must execute precise and complex maneuvers within a limited space. Cl…
Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
Kohio Deflesselle, Mélodie Daniel, Aly Magassouba +2
We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike hol…
Beyond Detection -- Orchestrating Human-Robot-Robot Assistance via an Internet of Robotic Things Paradigm
Joseph Hunt, Koyo Fujii, Aly Magassouba +1
Hospital patient falls remain a critical and costly challenge worldwide. While conventional fall prevention systems typically rely on post-fall detection or reactive alerts, they a…
Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objects
Mélodie Daniel, Aly Magassouba, Miguel Aranda +4
Robotic manipulation of deformable linear objects (DLOs) has great potential for applications in diverse fields such as agriculture or industry. However, a major challenge lies in…
CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation
Aly Magassouba, Komei Sugiura, Hisashi Kawai
Navigation guided by natural language instructions is particularly suitable for Domestic Service Robots that interacts naturally with users. This task involves the prediction of a…
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
Aly Magassouba, Komei Sugiura, Angelica Nakayama +4
Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. Th…
A Multimodal Target-Source Classifier with Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects
Aly Magassouba, Komei Sugiura, Hisashi Kawai
In this study, we focus on multimodal language understanding for fetching instructions in the domestic service robots context. This task consists of predicting a target object, as…
Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target-Source Classification
Aly Magassouba, Komei Sugiura, Anh Trinh Quoc +1
In this paper, we address multimodal language understanding for unconstrained fetching instruction in domestic service robots context. A typical fetching instruction such as "Bring…
A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions
Aly Magassouba, Komei Sugiura, Hisashi Kawai
This paper focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots. We address the case of ambiguous instructions, that is, whe…