16 citations · 37 across the 17 of their papers we have counts for
11 papers · 1 filter
AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
Ran Gong, Xiaohan Zhang, Jinghuan Shang +11
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has beco…
Sceniris: A Fast Procedural Scene Generation Framework
Jinghuan Shang, Harsh Patel, Ran Gong +1
Synthetic 3D scenes are essential for developing Physical AI and generative models. Existing procedural generation methods often have low output throughput, creating a significant…
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
Jad Abou-Chakra, Lingfeng Sun, Krishan Rana +5
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, which lack the ability to safely test policies…
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
Ondrej Biza, Thomas Weng, Lingfeng Sun +6
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…
Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification…
Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
Haojie Huang, Karl Schmeckpeper, Dian Wang +6
Humans can imagine goal states during planning and perform actions to match those goals. In this work, we propose Imagination Policy, a novel multi-task key-frame policy network fo…
Uncertainty-driven Planner for Exploration and Navigation
Georgios Georgakis, Bernadette Bucher, Anton Arapin +3
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constit…
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper +5
Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which ca…
An Adversarial Objective for Scalable Exploration
Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni +1
Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature. Exis…
Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback
Vasileios Vasilopoulos, Georgios Pavlakos, Karl Schmeckpeper +2
This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object recognition to recast prior geometr…
RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian +6
Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the pro…