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Found 15 papers, 14 papers with code
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems.
Reparameterized Policy Learning for Multimodal Trajectory Optimization
We then present a practical model-based RL method, called Reparameterized Policy Gradient (RPG), which leverages the multimodal policy parameterization and learned world model to achieve strong exploration capabilities and high data efficiency.
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution.
On the Efficacy of 3D Point Cloud Reinforcement Learning
Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations.
Deductive Verification of Chain-of-Thought Reasoning
In light of this, we propose to decompose a reasoning verification process into a series of step-by-step subprocesses, each only receiving their necessary context and premises.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Ranked #5 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet40
(using extra training data)
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI.
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds
We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds.
Improving Policy Optimization with Generalist-Specialist Learning
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations.
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Permutations then serve as target generation orders for training an insertion-based Transformer language model.
ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
Here we propose SAPIEN Manipulation Skill Benchmark (ManiSkill) to benchmark manipulation skills over diverse objects in a full-physics simulator.
Discovering Autoregressive Orderings with Variational Inference
One strategy to recover this information is to decode both the content and location of tokens.
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
Regularization Matters in Policy Optimization
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
