Define the task
Clarify what the robot should do and which environmental changes matter most.
APPLICATIONS
For tasks that change.
For capabilities that grow.
From manipulation to adaptation, explore how robots could respond to change. Concept scenarios below.
ASSEMBLY / CONCEPT SCENARIO
Adaptive action strategies
Manipulation & assembly
Position differences, changing objects, and contact interactions can make fixed action sequences difficult to reuse.
What we are exploring
Explore adjustments during grasping, alignment, and manipulation, using execution feedback to inform strategy refinement.
SORTING / CONCEPT SCENARIO
Handling changing objects
Sorting & handling
As object poses, placement, and task order change, robots need to reassess and replan.
What we are exploring
Explore connections between perception, action selection, and real feedback to adapt to changing sorting and handling tasks.
ADAPTATION / CONCEPT SCENARIO
Adapting to tasks and environments
Changing environments
Changes to layouts, obstacles, and goals can require new paths or adjustments to fixed strategies.
What we are exploring
Start from a new environment or task, explore feasible actions, and revise the plan based on execution outcomes.
Concept scenarios and illustrations, not delivered projects, confirmed hardware support, or performance guarantees.
FROM A TASK TO A LEARNING LOOP
Start with the real problem.
Define a path to validation.
Build a learning loop that can be evaluated against a real task.
Set the boundaries
Define the hardware conditions, operating constraints, and criteria for success.
Validate the approach
Explore how virtual training and real feedback could form a loop around a concrete task.
Iterate over time
Use validation outcomes to identify improvements and evaluate further adaptation and expansion.
In your workflow,
what keeps changing?
Let’s discuss possible collaboration around robot platforms, concrete tasks, and real-world feedback.