Embodied AI Engineer Interview Questions | Generated by AI
Here’s a comprehensive set of interview questions tailored for an Embodied AI Engineer role, with strong focus on modern visuomotor policy learning, especially Diffusion Policy, , **RT-2, ACT (Action Chunking Transformer), and general embodied intelligence technologies.
General Embodied AI & Robotics Foundation
- What are the key differences between embodied AI and traditional “disembodied” large language/vision models?
- Why is sim-to-real transfer significantly harder in embodied AI compared to computer vision or NLP tasks?
- Compare data requirements and scaling laws you’ve observed in embodied AI versus in LLMs or vision foundation models.
- How do you think about safety and robustness in real-world robotic deployment (e.g., failure modes, recovery behaviors)?
Diffusion Policy (UC Berkeley, Chi et al., 2023–2025)
- Explain the core idea of Diffusion Policy and why diffusion models are particularly well-suited for visuomotor control.
- Walk through the forward/reverse process when using a diffusion model as a policy. How is action denoising conditioned on visual observations?
- What are the main advantages of Diffusion Policy over previous imitation-learning baselines (e.g., behavioral cloning with MSE, GCBC, Transformer BC)?
- Diffusion Policy often uses a U-Net backbone with FiLM conditioning or cross-attention. Compare these two visual conditioning methods in terms of performance and inference speed.
- How does classifier-free guidance work in Diffusion Policy, and how does it affect exploration vs exploitation at test time?
- In the 2024–2025 versions, Diffusion Policy has been combined with scene-graph or language conditioning. How would you add high-level language goals to a diffusion policy?
- What are common failure modes you’ve seen with Diffusion Policy in real-robot deployments and how were they mitigated?
RT-2 (Google DeepMind, 2023–2024)
- What is RT-2 and how does it co-fine-tune a vision-language model (PaLI-X / PaLM-E) into a robotic actions?
- Explain the tokenization scheme used in RT-2 for continuous actions. Why discretize actions into bins?
- RT-2 claims emergent capabilities (e.g., chain-of-thought reasoning, arithmetic, symbol understanding) transferred to robotics. Have you reproduced or observed these in practice?
- Compare RT-2 with OpenVLA and Octo. In which scenarios would you prefer RT-2 over the others?
- How does RT-2 handle long-horizon tasks and multi-task generalization compared to Diffusion Policy or ACT?
ACT (Action Chunking Transformer, Tony Zhao et al., 2023)
- What problem does action chunking solve in transformer-based policies, and why is chunking critical for real-time control at 10–50 Hz?
- Describe the ACT architecture: how are actions chunked, how is the latent target computed, and how is variance modeled?
- Compare ACT with Diffusion Policy in terms of sample efficiency, inference speed, and success rate on contact-rich tasks.
- ACT originally used CVAE for latent modeling, later versions use flow-matching or diffusion. What benefits did the newer versions bring?
Broader Visuomotor Policy Landscape
- Compare the four major visuomotor policy families in 2024–2025:
- Transformer sequence models (ACT, Octo)
- Diffusion Policy family
- VLA-style models (RT-2, OpenVLA, Octo-Transformer)
- Flow-matching policies (e.g., MIMo, Aurora)
- When would you choose flow-matching over diffusion for a real-time robot (e.g., humanoid or mobile manipulator)?
- How do recent models like Octo (UC Berkeley, 2024) and OpenVLA (Stanford/PMI, 2024) combine strengths of ACT and RT-2?
- What role do you see foundation models (e.g., embedding actions into the same space as language/image tokens) playing in the next 2–3 years of embodied AI?
System Design & Engineering Questions
- Design a full training pipeline for collecting a 100k+ episode real-world dataset for a bimanual mobile manipulator using Diffusion Policy or ACT.
- How would you make a visuomotor policy run in real-time (≥30 Hz) on an edge GPU (e.g., Jetson Orin) while keeping high success rate?
- You have a policy that works perfectly in simulation but completely fails on the real robot. Systematic debugging checklist?
- How would you add force/tactile sensing as additional modalities to an existing vision-only Diffusion Policy or RT-2 model?
- Explain how you would do fast adaptation (few-shot or zero-shot) to a new object instance or new background using any of these methods.
Research & Future Directions
- Where do you think the field of embodied AI will be in 2027—will we have generalist home robots? What is the biggest blocker today?
- Do you believe the current imitation-learning + internet-scale pre-training paradigm (OpenVLA, Octo, etc.) is sufficient, or do true generalization, or do we still need reinforcement learning or active data collection?
These questions range from foundational knowledge to cutting-edge research details and practical engineering challenges—perfect for mid-to-senior embodied AI engineer or researcher interviews in 2025. Good luck with your interviews!