R
Roberto Martín-Martín
Researcher at Stanford University
Publications - 80
Citations - 3726
Roberto Martín-Martín is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 20, co-authored 75 publications receiving 1884 citations. Previous affiliations of Roberto Martín-Martín include Technical University of Berlin.
Papers
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Proceedings ArticleDOI
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
TL;DR: DenseFusion as mentioned in this paper proposes a heterogeneous architecture that processes the two complementary data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated.
Proceedings Article
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
Vineet Kosaraju,Amir Sadeghian,Roberto Martín-Martín,Ian Reid,Hamid Rezatofighi,Hamid Rezatofighi,Silvio Savarese +6 more
TL;DR: A graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene and achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks.
Posted Content
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
Vineet Kosaraju,Amir Sadeghian,Roberto Martín-Martín,Ian Reid,Hamid Rezatofighi,Hamid Rezatofighi,Silvio Savarese +6 more
TL;DR: In this paper, a graph-based generative adversarial network is proposed to predict multimodal trajectories of multiple interacting agents in a scene by better modeling the social interactions of pedestrians in the scene.
Posted Content
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning.
TL;DR: The key system modules and the benchmark environments of the new release robosuite v1.0 are discussed.
Proceedings ArticleDOI
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
Roberto Martín-Martín,Michelle A. Lee,Rachel Gardner,Silvio Savarese,Jeannette Bohg,Animesh Garg +5 more
TL;DR: It is shown that VICES improves sample efficiency, maintains low energy consumption, and ensures safety across all three experimental setups, and RL policies learned with VICES can transfer across different robot models in simulation, and from simulation to real for the same robot.