2 citations · 2 across the 2 of their papers we have counts for
5 papers
DeFM: Learning Foundation Representations from Depth for Robotics
Manthan Patel, Jonas Frey, Mayank Mittal +5
Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achi…
SAHA: Supervised Autonomous HArvester for selective forest thinning
Fang Nan, Meher Malladi, Qingqing Li +7
Forestry plays a vital role in our society, creating significant ecological, economic, and recreational value. Efficient forest management involves labor-intensive and complex oper…
Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning
Fan Yang, Per Frivik, David Hoeller +3
Recent advancements in robot navigation, particularly with end-to-end learning approaches such as reinforcement learning (RL), have demonstrated strong performance. However, succes…
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
Manthan Patel, Fan Yang, Yuheng Qiu +4
We present TartanGround, a large-scale, multi-modal dataset to advance the perception and autonomy of ground robots operating in diverse environments. This dataset, collected in va…
Representation Convergence: Mutual Distillation is Secretly a Form of Regularization
Zhengpeng Xie, Jiahang Cao, Changwei Wang +5
In this paper, we argue that mutual distillation between reinforcement learning policies serves as an implicit regularization, preventing them from overfitting to irrelevant featur…