| 1 |
Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments |
提出基于超网络的MPC局部轨迹规划方法，用于未知环境下的最大安全集学习。 |
MPC model predictive control |
|
|
| 2 |
Discovering Robotic Interaction Modes with Discrete Representation Learning |
ActAIM2：通过离散表示学习发现机器人交互模式，提升操作能力。 |
manipulation representation learning privileged information |
✅ |
|
| 3 |
Neural Fields in Robotics: A Survey |
综述：神经场在机器人领域的应用，提升感知、规划与控制能力 |
manipulation gaussian splatting splatting |
|
|
| 4 |
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images |
GHIL-Glue：通过过滤子目标图像实现分层控制，提升机器人泛化能力。 |
manipulation imitation learning language conditioned |
|
|
| 5 |
NeoPhysIx: An Ultra Fast 3D Physical Simulator as Development Tool for AI Algorithms |
NeoPhysIx：超快速3D物理模拟器，加速AI算法开发与训练 |
legged robot reinforcement learning |
|
|
| 6 |
FRTree Planner: Robot Navigation in Cluttered and Unknown Environments with Tree of Free Regions |
提出FRTree规划器，解决复杂未知环境下机器人狭窄通道导航问题 |
trajectory optimization |
|
|