Robot Tasks: From Navigation to Manipulation

Robotics encompasses a wide spectrum of tasks, each with distinct objectives, evaluation metrics, and state-of-the-art methods. This module provides a systematic overview of the major task categories in modern robotics research, with emphasis on what each task requires, how it is benchmarked, and where the knowledge applies.

Task Taxonomy

Robot Tasks
├── Navigation
│   ├── Point-Goal Navigation (PointNav)
│   ├── Object-Goal Navigation (ObjectNav)
│   ├── Vision-Language Navigation (VLN)
│   ├── Exploration / Active Mapping
│   ├── Social Navigation
│   └── SLAM (see dedicated chapter)
│
├── Manipulation
│   ├── Pick-and-Place
│   ├── Assembly
│   ├── Dexterous Manipulation
│   ├── Deformable Object Manipulation
│   ├── Tool Use
│   └── Mobile Manipulation
│
├── Task & Motion Planning (TAMP)
│   ├── Hierarchical Planning
│   └── LLM-based Task Planning
│
├── Language Grounding
│   ├── Embodied Question Answering (EQA)
│   ├── Instruction Following
│   └── Language-Conditioned Manipulation
│
└── Multi-Agent & Social
    ├── Collaborative Manipulation
    ├── Human-Robot Interaction
    └── Multi-Robot Coordination

Quick Comparison

Task Category	Key Challenge	Primary Sensor	Top Simulators	Key Datasets
Navigation	Spatial reasoning, exploration	RGB-D, LiDAR	Habitat, AI2-THOR, Gibson	Matterport3D, HM3D, ScanNet
SLAM	Localization + mapping	Camera, LiDAR, IMU	Gazebo, Isaac Sim	TUM RGB-D, KITTI, EuRoC
Manipulation	Grasping, contact-rich control	RGB-D, tactile	MuJoCo, Isaac, SAPIEN	YCB, DexYCB, RLBench
TAMP	Long-horizon reasoning	Any	ALFRED, Behavior-1K	Open X-Embodiment
Language Grounding	Vision-language alignment	RGB, language	AI2-THOR, Habitat	R2R, REVERIE, ALFRED
Multi-Agent	Coordination, communication	Multi-robot	Habitat 3.0, RoboCasa	SCAND, BEHAVIOR

Where This Knowledge Applies

Understanding these task categories is essential for:

1. Research direction: Choosing which problems to work on based on current gaps
2. System design: Selecting the right sensors, algorithms, and evaluation metrics
3. Benchmarking: Comparing methods fairly using standard datasets and simulators
4. Sim-to-real transfer: Choosing simulators that match your target domain
5. Curriculum design: Building progressive learning paths for robot learning

Landmark Survey Papers

These surveys provide comprehensive overviews of the field:

• Embodied AI: A Survey of Recent Advances and Future Directions (2024) — Broad taxonomy covering navigation, manipulation, and planning
• Foundations and Recent Trends in Embodied AI (2024) — From perception to multi-agent systems
• A Survey on Vision-Language Navigation (Guan et al., 2022) — Deep dive into VLN tasks and methods
• Core Challenges of Social Robot Navigation (Mavrogiannis et al., 2022) — ACM Computing Surveys
• Open X-Embodiment (Google DeepMind, 2024) — Cross-embodiment dataset with 1M+ trajectories from 22 robots

Chapter Guide

Chapter	Content
Navigation	PointNav, ObjectNav, VLN, Exploration, Social Nav
SLAM	Visual SLAM, LiDAR SLAM, datasets, evaluation
Manipulation	Grasping, assembly, dexterous, deformable, tool use
Datasets & Benchmarks	Comprehensive reference of all major datasets

References

• Anderson et al. (2018). "On Evaluation of Embodied Navigation Agents." arXiv:1807.06757
• Batra et al. (2020). "Exploring Visual Navigation using Habitat." arXiv:2004.01261
• Savva et al. (2019). "Habitat: A Platform for Embodied AI Research." ICCV 2019
• CVPR 2024 Embodied AI Workshop