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Found 10 papers, 5 papers with code
OpenAI o1 System Card
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.
Jump-Start Reinforcement Learning
In addition, we provide an upper bound on the sample complexity of JSRL and show that with the help of a guide-policy, one can improve the sample complexity for non-optimism exploration methods from exponential in horizon to polynomial.
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
GRAC: Self-Guided and Self-Regularized Actor-Critic
One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function.
How to Close Sim-Real Gap? Transfer with Segmentation!
Combining the control policy learned from simulation with the perception model, we achieve an impressive $\bf{88\%}$ success rate in grasping a tiny sphere with a real robot.
Robotics
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
Challenges in taking the state-space approach are the estimation of the high-dimensional state of a deformable object from raw images, where annotations are very expensive on real data, and finding a dynamics model that is both accurate, generalizable, and efficient to compute.
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Understanding dynamic 3D environment is crucial for robotic agents and many other applications.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
Ranked #3 on
3D Object Recognition
on ModelNet40
3D Reconstruction from Full-view Fisheye Camera
In this report, we proposed a 3D reconstruction method for the full-view fisheye camera.
