The End Of Knowledge - Vault 6/36 - CVPR - 2018 - Collaborative Robots: Challenges and Opportunities.

graph LR classDef interaction fill:#f9d4d4, font-weight:bold, font-size:14px classDef grasping fill:#d4f9d4, font-weight:bold, font-size:14px classDef human fill:#d4d4f9, font-weight:bold, font-size:14px classDef learning fill:#f9f9d4, font-weight:bold, font-size:14px classDef advanced fill:#f9d4f9, font-weight:bold, font-size:14px Main[Collaborative Robots: Challenges
and Opportunities.] Main --> A[Robots interacting through
manipulation and collaboration 1] A --> B[Object grasping and
manipulation techniques 2] B --> C[In-hand manipulation for
better task performance 3] B --> D[Human grasping informs
robotic strategies 4] B --> E[Task-based grasp selection 5] A --> F[Common sense reasoning
for planning 6] F --> G[Hand tracking systems 7] G --> H[Predicting human intentions
for collaboration 8] G --> I[Manifold learning for
hand actions 9] Main --> J[Robotic hand design
optimization 10] J --> K[Grasp moduli spaces
for transfer learning 11] J --> L[Temporal grasping: continuous
observation and adaptation 12] Main --> M[Dual-arm manipulation techniques 13] M --> N[Cluttered environment interaction
strategies 14] N --> O[Non-prehensile rearrangement planning 15] Main --> P[Human motion prediction
for collaboration 16] P --> Q[Conditional variational autoencoders
for motion generation 17] P --> R[Learning from demonstration 18] R --> S[Reinforcement learning for
adaptive behaviors 19] Main --> T[Efficient learning in
robotics 20] T --> U[Dimensionality reduction via
encoder-decoder networks 21] T --> V[Sim-to-real knowledge transfer 22] Main --> W[Embodiment understanding 23] W --> X[Integrated perception and
action systems 24] X --> Y[Multi-sensory integration for
improved control 25] Main --> Z[Natural human-robot interaction 26] Z --> AA[Adaptive robot behavior 27] Z --> AB[Hierarchical learning for
abstraction levels 28] Main --> AC[3D representation for
tracking and interaction 29] AC --> AD[Dexterous manipulation similar
to human abilities 30] class A,F interaction class B,C,D,E,J,K,L grasping class G,H,I,P,Q,R human class S,T,U,V learning class M,N,O,W,X,Y,Z,AA,AB,AC,AD advanced

Resume:

1.- Physical robot interaction: Developing robots capable of interacting with the world, humans, and each other through physical manipulation and collaboration.

2.- Object grasping and manipulation: Enabling robots to grasp and manipulate objects effectively, considering task requirements and object properties.

3.- In-hand manipulation: Developing techniques for robots to adjust object poses within their grasp for better task performance.

4.- Human grasping understanding: Studying human grasping to inform robotic grasping strategies and prosthetic design.

5.- Task-related grasping: Choosing appropriate grasps based on the intended task and object properties.

6.- Common sense reasoning: Incorporating contextual understanding and reasoning into robotic planning and decision-making processes.

7.- Hand tracking: Developing systems to track human hand movements and poses during object interaction.

8.- Predicting human intentions: Creating algorithms to anticipate human actions for improved human-robot collaboration.

9.- Manifold learning: Using techniques like GPLVM to understand the structure of human hand actions.

10.- Robotic hand design: Optimizing robotic hand designs based on human hand action manifolds and task requirements.

11.- Grasp moduli spaces: Developing a common representation for grasps and object shapes to enable transfer learning.

12.- Temporal grasping: Considering grasping as a continuous process involving observation, execution, and adaptation.

13.- Dual-arm manipulation: Exploring techniques for robots to use two arms cooperatively for complex manipulation tasks.

14.- Cluttered environment interaction: Developing strategies for robots to operate effectively in crowded or constrained spaces.

15.- Non-prehensile rearrangement planning: Planning push actions to rearrange objects without grasping them.

16.- Human motion prediction: Using machine learning techniques to predict future human movements for improved collaboration.

17.- Conditional variational autoencoders: Applying probabilistic models to generate distributions of possible future human motions.

18.- Learning from demonstration: Enabling robots to learn tasks by observing and interacting with humans.

19.- Reinforcement learning for human-robot interaction: Using RL to teach robots adaptive behaviors for collaborative tasks.

20.- Efficient learning in robotics: Developing techniques to reduce the amount of real-world data needed for robot learning.

21.- Dimensionality reduction in learning: Using encoder-decoder networks to simplify learning problems in high-dimensional spaces.

22.- Sim-to-real transfer: Transferring knowledge gained in simulation to real-world robotic systems.

23.- Embodiment understanding: Assessing a robot's capabilities based on its physical structure and degrees of freedom.

24.- Integrated perception and action: Developing systems that seamlessly connect perception, planning, and execution.

25.- Multi-sensory integration: Combining visual, tactile, and force feedback for improved robot perception and control.

26.- Natural human-robot interaction: Creating robot behaviors that feel intuitive and comfortable for human collaborators.

27.- Adaptive robot behavior: Enabling robots to adjust their actions based on human behavior and task context.

28.- Hierarchical learning: Structuring learning problems to address different levels of abstraction and complexity.

29.- 3D representation: Utilizing 3D information for improved human tracking and object interaction.

30.- Dexterous manipulation: Developing robotic systems capable of fine, precise object manipulation similar to human abilities.

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