2 citations · 5 across the 10 of their papers we have counts for
12 papers
Advantage-Driven Explicit Memory for Social Navigation
Yeonsoo Park, Mattia Racca, Guillaume Bono +4
Robot policies are predominantly learned with classical parametric variants of imitation learning or RL, where training stores the agent's behavior exclusively in the policy's netw…
Compressing History into Memory: Distilling Transformers into Recurrent Transformers
Philippe Weinzaepfel, Christian Wolf, Bülent Mert Sariyildiz +3
Transformers are AI's workhorse but their computational cost becomes prohibitive when processing long sequences. We target long-horizon streaming vision and robotics applications,…
Kinaema: a recurrent sequence model for memory and pose in motion
Mert Bulent Sariyildiz, Philippe Weinzaepfel, Guillaume Bono +2
One key aspect of spatially aware robots is the ability to "find their bearings", ie. to correctly situate themselves in previously seen spaces. In this work, we focus on this part…
Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
Steeven Janny, Hervé Poirier, Leonid Antsfeld +6
Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-condition…
RANa: Retrieval-Augmented Navigation
Gianluca Monaci, Rafael S. Rezende, Romain Deffayet +5
Methods for navigation based on large-scale learning typically treat each episode as a new problem, where the agent is spawned with a clean memory in an unknown environment. While…
Learning to navigate efficiently and precisely in real environments
Guillaume Bono, Hervé Poirier, Leonid Antsfeld +3
In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and…
Multi-Object Navigation in real environments using hybrid policies
Assem Sadek, Guillaume Bono, Boris Chidlovskii +2
Navigation has been classically solved in robotics through the combination of SLAM and planning. More recently, beyond waypoint planning, problems involving significant components…
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
Guillaume Bono, Leonid Antsfeld, Boris Chidlovskii +2
Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations g…
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
Guillaume Bono, Leonid Antsfeld, Assem Sadek +2
Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision takin…
MoCap-less Quantitative Evaluation of Ego-Pose Estimation Without Ground Truth Measurements
Quentin Possamaï, Steeven Janny, Guillaume Bono +3
The emergence of data-driven approaches for control and planning in robotics have highlighted the need for developing experimental robotic platforms for data collection. However, t…
An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments
Assem Sadek, Guillaume Bono, Boris Chidlovskii +1
Visual navigation by mobile robots is classically tackled through SLAM plus optimal planning, and more recently through end-to-end training of policies implemented as deep networks…
SIM2REALVIZ: Visualizing the Sim2Real Gap in Robot Ego-Pose Estimation
Theo Jaunet, Guillaume Bono, Romain Vuillemot +1
The Robotics community has started to heavily rely on increasingly realistic 3D simulators for large-scale training of robots on massive amounts of data. But once robots are deploy…