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References and Citations

Curated list of academic papers, official documentation, and authoritative resources referenced throughout the course.

Official Documentation​

ROS 2​

NVIDIA Isaac​

Simulation Platforms​

Hardware​

Foundational Papers​

Humanoid Robotics​

  1. Kajita, S., et al. (2003). "Biped walking pattern generation by using preview control of zero-moment point." IEEE International Conference on Robotics and Automation.

    • Foundation for bipedal walking control using ZMP

  2. Vukobratović, M., & Borovac, B. (2004). "Zero-moment point—thirty five years of its life." International Journal of Humanoid Robotics.

    • Historical overview of ZMP concept

  3. Hirose, M., & Ogawa, K. (2007). "Honda humanoid robots development." Philosophical Transactions of the Royal Society A.

    • Development history of ASIMO and humanoid research

Reinforcement Learning for Robotics​

  1. Schulman, J., et al. (2017). "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347.

    • PPO algorithm widely used in robot control

  2. Haarnoja, T., et al. (2018). "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." ICML.

    • SAC algorithm for continuous control

  3. OpenAI, et al. (2019). "Solving Rubik's Cube with a robot hand." arXiv preprint arXiv:1910.07113.

    • Landmark sim-to-real transfer demonstration

Sim-to-Real Transfer​

  1. Tobin, J., et al. (2017). "Domain randomization for transferring deep neural networks from simulation to the real world." IROS.

    • Key technique for bridging sim-to-real gap

  2. Tan, J., et al. (2018). "Sim-to-real: Learning agile locomotion for quadruped robots." RSS.

    • Successful transfer of locomotion policies

  3. Peng, X. B., et al. (2020). "Learning agile robotic locomotion skills by imitating animals." RSS.

    • Imitation learning from animal motion

Vision-Language-Action Models​

Vision-Language Models​

  1. Radford, A., et al. (2021). "Learning transferable visual models from natural language supervision." ICML (CLIP).

    • Foundation for vision-language understanding

  2. Alayrac, J. B., et al. (2022). "Flamingo: a visual language model for few-shot learning." NeurIPS.

    • Multi-modal vision-language architecture

Robotics Foundation Models​

  1. Brohan, A., et al. (2022). "RT-1: Robotics transformer for real-world control at scale." arXiv preprint arXiv:2212.06817.

    • Google's robotics transformer model

  2. Brohan, A., et al. (2023). "RT-2: Vision-language-action models transfer web knowledge to robotic control." CoRL.

    • Scaling vision-language models to robotics

  3. Octo Model Team (2024). "Octo: An open-source generalist robot policy." arXiv preprint.

    • Open-source VLA model

Embodied AI​

  1. Deitke, M., et al. (2022). "Behavior-1K: A Benchmark for Embodied AI with 1,000 Everyday Activities and Realistic Simulation." CoRL.

    • Comprehensive embodied AI benchmark

  2. Szot, A., et al. (2021). "Habitat 2.0: Training home assistants to rearrange their habitat." NeurIPS.

    • Simulation platform for embodied AI research

Perception and SLAM​

Visual SLAM​

  1. Mur-Artal, R., et al. (2015). "ORB-SLAM: a versatile and accurate monocular SLAM system." IEEE Transactions on Robotics.

    • Influential monocular SLAM system

  2. Campos, C., et al. (2021). "ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM." IEEE Transactions on Robotics.

    • Multi-sensor SLAM framework

Object Detection​

  1. Redmon, J., et al. (2016). "You only look once: Unified, real-time object detection." CVPR (YOLO).

    • Real-time object detection breakthrough

  2. He, K., et al. (2017). "Mask R-CNN." ICCV.

    • Instance segmentation for robotics

Manipulation and Grasping​

  1. Levine, S., et al. (2018). "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." International Journal of Robotics Research.

    • Data-driven grasping approaches

  2. Mahler, J., et al. (2017). "Dex-Net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics." RSS.

    • Grasp planning with deep learning

  3. Morrison, D., et al. (2020). "Learning robust, real-time, reactive robotic grasping." International Journal of Robotics Research.

    • Reactive grasping strategies

Natural Language Processing for Robotics​

  1. Tellex, S., et al. (2011). "Understanding natural language commands for robotic navigation and mobile manipulation." AAAI.

    • Natural language grounding for robots

  2. Ahn, M., et al. (2022). "Do as I can, not as I say: Grounding language in robotic affordances." CoRL (SayCan).

    • LLM-based robot task planning

  3. Huang, W., et al. (2022). "Language models as zero-shot planners: Extracting actionable knowledge for embodied agents." ICML.

    • Using LLMs for robot planning

Books and Textbooks​

Robotics Fundamentals​

  • Siciliano, B., et al. (2009). Robotics: Modelling, Planning and Control. Springer.
  • Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control. Pearson.
  • Thrun, S., et al. (2005). Probabilistic Robotics. MIT Press.

ROS and Robot Programming​

  • Quigley, M., et al. (2015). Programming Robots with ROS. O'Reilly Media.
  • Martinez, A., & Fernández, E. (2021). A Concise Introduction to Robot Programming with ROS2. CRC Press.

AI and Machine Learning​

  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
  • Goodfellow, I., et al. (2016). Deep Learning. MIT Press.

Standards and Specifications​

Industry Reports and Whitepapers​

  • NVIDIA (2023). "Isaac Sim for Robotics Development" - Technical whitepaper
  • Boston Dynamics (2022). "Atlas: The Next Generation" - Technical overview
  • OpenAI (2023). "GPT-4 Technical Report" - Model capabilities and safety

Open-Source Repositories​

Robot Models and Datasets​

Software Libraries​

Conference and Journal Resources​

Key Conferences​

  • ICRA (International Conference on Robotics and Automation) - IEEE flagship robotics conference
  • IROS (International Conference on Intelligent Robots and Systems) - IEEE robotics conference
  • RSS (Robotics: Science and Systems) - Leading robotics research conference
  • CoRL (Conference on Robot Learning) - Machine learning for robotics
  • NeurIPS (Neural Information Processing Systems) - AI/ML conference with robotics track

Key Journals​

  • IEEE Transactions on Robotics
  • International Journal of Robotics Research
  • Autonomous Robots
  • Robotics and Autonomous Systems

Online Courses​

Citation Style​

This course uses IEEE citation style. For citing these references in your assignments:

[1] S. Kajita et al., "Biped walking pattern generation by using preview
    control of zero-moment point," in Proc. IEEE Int. Conf. Robot.
    Autom. (ICRA), 2003, pp. 1620-1626.