Physical AI

Transform a 3D point between robot coordinate frames using homogeneous transformations.

Compute the forward kinematics of a simple articulated arm.

Use a manipulator Jacobian to relate joint velocities to end-effector velocity.

Solve a simple inverse-kinematics target using an iterative Jacobian method.

Implement closed-loop proportional-derivative control for a simulated joint.

Compare position, velocity, and torque control for a specified embodied task.

Explain how time steps, collision geometry, friction, and contact models affect a physics simulation.

Configure simulated bodies, joints, actuators, and sensors for an embodied experiment.

Implement the reset, observation, action, and step logic of a simulation environment.

Test whether a simulation environment is deterministic under fixed seeds and initial conditions.

Diagnose unstable or unrealistic behavior caused by common simulation settings.

Record reproducible policy rollouts with synchronized actions, observations, rewards, and metadata.

Project depth-image pixels into 3D points in a robot coordinate frame.

Combine visual observations with proprioceptive state for policy input.

Estimate an object pose from noisy visual measurements.

Perform one prediction-and-update cycle of a Kalman filter for robot state estimation.

Evaluate a perception pipeline under noise, occlusion, and viewpoint changes.

Choose between modular perception and end-to-end representation learning for a given task.

Construct a two-dimensional occupancy grid from simulated range observations.

Apply a Bayesian localization update to a mobile robot pose estimate.

Find a collision-free grid path using the A* algorithm.

Convert a planned path into local velocity commands for a mobile robot.

Specify an observation-conditioned policy for goal-directed navigation under partial observability.

Evaluate navigation with success, collision, path-efficiency, and robustness metrics.

Represent a candidate robot grasp using position, orientation, and gripper width.

Generate grasp candidates from an object point cloud.

Reject grasp or motion candidates that violate reachability or collision constraints.

Plan a collision-free joint-space path to a target end-effector pose.

Construct a closed-loop pick-and-place sequence using visual feedback.

Classify manipulation failures as perception, planning, control, or interaction errors.

Select an on-policy, off-policy, or model-based learning approach for a specified embodied task.

Design a curriculum that increases task difficulty without changing the target behavior.

Apply goal relabeling to create useful training examples from unsuccessful trajectories.

Construct a recurrent policy input-state update for a partially observable task.

Use a learned dynamics model to evaluate short candidate action sequences.

Compose reusable low-level skills into a hierarchical policy.

Compare policies using sample efficiency, final performance, and out-of-distribution generalization.

Train a behavior-cloning policy from observation-action demonstrations.

Diagnose compounding error caused by distribution shift in behavior cloning.

Explain how interactive data collection such as DAgger reduces compounding error.

Align a language instruction with task-relevant visual and proprioceptive information.

Describe how a vision-language-action model converts multimodal context into robot actions.

Select an adaptation strategy for applying a pretrained embodied model to a new task.

Evaluate whether a policy follows instructions while remaining grounded in the physical scene.

Identify visual, dynamical, sensing, and timing sources of the simulation-to-reality gap.

Design domain-randomization ranges for a specified deployment environment.

Estimate selected simulator parameters from observed robot trajectories.

Choose whether to transfer a representation, dynamics model, or policy for a given setting.

Specify operational constraints that prevent unsafe actions during policy execution.

Use uncertainty or out-of-distribution signals to trigger a safe fallback behavior.

Design an evaluation split that tests robustness and generalization rather than memorization.

Create a reproducibility checklist covering code, data, seeds, configurations, and metrics.

Define a bounded embodied-AI project with measurable success and robustness criteria.

Decompose the project into perception, estimation, planning, control, and learning components.

Choose a modular, end-to-end, or hybrid architecture for the project.

Specify a data-collection and training plan that matches the project’s failure risks.

Design ablation experiments that isolate the contribution of major system components.

Perform structured error analysis on failed embodied-agent rollouts.

Decide whether simulation results justify transfer to physical hardware.