Introduction to Robotics

Compare main actuator types (e.g., electric, hydraulic) and their trade-offs in cost, power, and precision.

Explain the function of transmissions and linkages in robot motion.

Evaluate how design choices for payload, speed, and precision constrain a robot's cost and application suitability.

Describe the core challenges of robotic manipulation and the role of end effectors (grippers, tools).

Distinguish between rigid and soft robotics approaches and their respective commercial use cases.

Compare major sensor types (e.g., Camera, LiDAR, IMU) based on their capabilities, limitations, and cost.

Explain sensor fusion and its role in improving perception reliability and robustness.

Describe core computer vision tasks in robotics, including depth perception, object detection, and segmentation.

Contrast traditional computer vision with deep learning-based approaches for perception.

Identify common failure modes of perception systems (e.g., lighting, weather, occlusion) and their operational impact.

Assess how a company's choice of sensor suite and perception software can create a technical moat or liability.

Explain SLAM (Simultaneous Localization and Mapping) and why it is fundamental to mobile robotics.

Describe different mapping representations (e.g., occupancy grids, point clouds) and their trade-offs.

Explain path planning conceptually and the trade-offs between different algorithmic approaches.

Contrast GPS-based navigation with techniques used in GPS-denied environments.

Identify key technical challenges that limit autonomous navigation in dynamic or unstructured environments.

Explain the role of closed-loop feedback control in achieving stable and precise robot motion.

Describe the control challenges in multi-joint coordination and force-sensitive manipulation.

Describe the typical robotics software stack and the role of middleware like ROS in development and integration.

Explain the importance of simulation and 'digital twins' for robot development, testing, and validation.

Assess how the quality of a robot's control and software stack impacts its performance, reliability, and safety.

Distinguish between rule-based systems and machine learning approaches in robotics.

Explain how supervised learning and imitation learning are used for tasks like perception and manipulation.

Explain reinforcement learning's role in autonomous skill acquisition and its practical challenges.

Describe the 'sim-to-real' problem and analyze a company's strategy for bridging this gap.

Assess the role and hype of foundation models (e.g., LLMs, Vision-Language Models) in robotics.

Identify the unique data collection and annotation challenges ('data moat') in robotics.

Develop a framework for critically assessing AI-related claims in a robotics startup's pitch.

Analyze how battery technology and power consumption constrain a robot's design and operational model.

Evaluate the trade-offs between on-board (edge) and cloud-based computation for robotic fleets.

Assess the role and limitations of communication technologies (e.g., WiFi, 5G) in large-scale robotics deployments.

Explain fleet management concepts for coordinating and optimizing multi-robot systems.

Classify the major types of warehouse robots (e.g., AMRs, AS/RS, picking arms) and their roles in a fulfillment center.

Analyze different navigation strategies (e.g., QR codes, LiDAR SLAM) as key technical differentiators for warehouse AMRs.

Analyze manipulation technologies (e.g., suction vs. grasping) as key technical differentiators for warehouse picking.

Assess the technical challenges of integrating robotics with warehouse management systems (WMS).

Evaluate the key factors driving the ROI and unit economics for warehouse robotics deployments.

Compare drone form factors (e.g., multirotor, fixed-wing, hybrid) and their performance trade-offs.

Evaluate the key technologies enabling autonomous drone flight (e.g., GPS, visual navigation, obstacle avoidance).

Identify the primary technical and operational challenges for major drone applications like delivery, inspection, and mapping.

Assess the impact of the regulatory landscape on the scalability of commercial drone operations.

Identify key technical differentiators and potential moats among competing drone companies.

Describe the current state of humanoid robot development and the key players in the space.

Explain the unique technical challenges of humanoid robotics, particularly in bipedal locomotion, dynamic balance, and energy efficiency.

Explain the challenges of creating general-purpose dexterous manipulation for humanoids.

Compare the investment thesis for general-purpose humanoids versus purpose-built robots for specific tasks.

Assess the technical maturity and near-term commercial viability of humanoid robotics ventures.

Develop a framework for identifying technical moats and sustainable differentiators in robotics companies.

Distinguish between technology readiness levels, from lab concept to scalable product.

Formulate key questions to evaluate a company's hardware scalability, including design for manufacturing and supply chain.

Formulate key questions to evaluate a company's software maturity, including stability, testing, and deployment processes.

Develop methods to assess the gap between a robot's performance in controlled demos versus real-world environments.

Analyze the unique cost drivers and revenue models in robotics (e.g., RaaS vs. hardware sales, service contracts).

Identify common technical red flags and over-hyped claims in robotics investment pitches.