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Found 17 papers, 4 papers with code
基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)
“在散文阅读理解的鉴赏类问题中, 对拟人句赏析考查比较频繁。目前, 已有的工作仅对拟人句中的本体要素进行识别并抽取, 存在要素抽取不完整的问题, 尤其是当句子中出现多个本体时, 需要确定拟人词与各个本体的对应关系。为解决这些问题, 本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征, 增强句子的向量表示, 再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上, 利用自注意力机制对要素之间的关系进行检测, 使用要素同步机制和关系同步机制进行信息交互, 用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行<本体, 拟人词>抽取的比较实验, 结果表明本文提出的模型性能优于其他比较模型。”
MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
MATCH POLICY is designed to solve high-precision tasks with a key-frame setting.
Equivariant Reinforcement Learning under Partial Observability
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions.
Equivariant Diffusion Policy
Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning.
Equivariant Offline Reinforcement Learning
Sample efficiency is critical when applying learning-based methods to robotic manipulation due to the high cost of collecting expert demonstrations and the challenges of on-robot policy learning through online Reinforcement Learning (RL).
Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
Humans can imagine goal states during planning and perform actions to match those goals.
Large Effects of Small Cues: Priming Selfish Economic Decisions
We find that when participants receive cues signaling that the decision has an economic context, both economics and non-economics students tend to maximize profits.
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Leveraging Symmetries in Pick and Place
This paper analytically studies the symmetries present in planar robotic pick and place and proposes a method of incorporating equivariant neural models into Transporter Net in a way that captures all symmetries.
On Robot Grasp Learning Using Equivariant Models
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware.
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture.
Leveraging Fully Observable Policies for Learning under Partial Observability
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information.
Partially Observable Reinforcement Learning
reinforcement-learning
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Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped.
Equivariant Grasp learning In Real Time
Visual grasp detection is a key problem in robotics where the agent must learn to model the grasp function, a mapping from an image of a scene onto a set of feasible grasp poses.
Action Priors for Large Action Spaces in Robotics
This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.
