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Found 19 papers, 7 papers with code
HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology.
Any-point Trajectory Modeling for Policy Learning
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning.
Learning Generalizable Tool-use Skills through Trajectory Generation
We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes.
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks
In this work, we present a focused study of the generalization capabilities of the pre-trained visual representations at the categorical level.
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking
In this work, we propose a self-supervised method to finetune a mesh reconstruction model in the real world.
Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation
Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels.
Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
We evaluate our system both on cloth flattening as well as on cloth canonicalization, in which the objective is to manipulate the cloth into a canonical pose.
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
We consider the problem of sequential robotic manipulation of deformable objects using tools.
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem.
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection
Therefore, we propose a unified Transformer-based framework for log anomaly detection (\ourmethod{}), which is comprised of the pretraining and adapter-based tuning stage.
Evaluating NLP Systems On a Novel Cloze Task: Judging the Plausibility of Possible Fillers in Instructional Texts
Cloze task is a widely used task to evaluate an NLP system's language understanding ability.
Learning Visible Connectivity Dynamics for Cloth Smoothing
Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions.
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
Adaptive Auxiliary Task Weighting for Reinforcement Learning
Reinforcement learning is known to be sample inefficient, preventing its application to many real-world problems, especially with high dimensional observations like images.
Reinforcement Learning without Ground-Truth State
However, if this policy is trained with reinforcement learning, then without a state estimator, it is hard to specify a reward function based on high-dimensional observations.
Adaptive Variance for Changing Sparse-Reward Environments
Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration.
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space.
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.
