8 papers
What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework
Hongze Wang, Boyang Sun, Jiaxu Xing +5
Object-Goal Navigation (ObjectNav) is a key capability for deploying mobile robots in everyday environments such as homes, schools, and workplaces. In this task, an agent must loca…
AME-2: Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding
Chong Zhang, Victor Klemm, Fan Yang +1
Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions and sparse footholds. Existing m…
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…
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…
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…