Embodied fieldwork
Next experiment: a repeatable inspection route, operator corrections and an inspectable episode trace.
Human intent.
Robot action.
Exploring how human intent becomes accountable robot action.
Independent experiments in interaction, orchestration and learning from failure.
INTERACTION · ORCHESTRATION · LEARNINGResearch in progress · London
01 / FEATURED EXPERIMENTBROWSER PROTOTYPE · SYNTHETIC EVENTS
A correction should change the task.
Explore a scripted episode: a person corrects a task, the harness holds execution, checks intent and records the next action.
EPISODE REPLAYSCRIPTED SCENARIO
SCHEMATIC FACILITY · SAMPLE POSITIONS
Built here
Browser playback, scenario switching, a task state inspector and a downloadable event trace.
What to test next
Interruption handling, correction accuracy and ownership handover on a connected robot.
Evidence boundary
A frontend demonstration with synthetic events. No physical robot, live model or trained policy is involved.
Six authored events · playback positions in seconds
02 / EXPERIMENT DIRECTIONSPROPOSED WORK · HARDWARE VALIDATION NEXT
From questions to experiments.
Continuous interaction
Next experiment: compare turn-taking and duplex interaction on the same correction task.
One research harness.
Different physical environments.
Research concepts · hardware validation remains a next step.
Notice what changed. Make the next step clear.
A quadruped revisits inspection points, flags a visual change and asks a person to confirm the task. The harness records the observation, decision and outcome.
WHAT TO MEASURE Detection quality · intervention rate · time to resolve
03 / THE HARNESSFROM INPUT TO ACCOUNTABLE ACTION
A conversation is
only the beginning.
The research question: how should human intent, robot observations and bounded skills meet? This proposed harness separates task decisions from local control, and episode review from policy rollout.
ILLUSTRATIVE ARCHITECTURESELECT A NODE TO EXPLORE ↗
ROBOT FEEDBACK →Versioned rollout ↩
Fast loop: observe → decide → bounded skill. Learning loop: episodes → training → validation.
A missed detection becomes a synchronized episode: video, robot state, action and outcome. Review the cause before changing the policy.
IN FOCUS
04 / RESEARCH NOTESQUESTIONS WORTH WORKING ON
Questions I’m working on.
05 / THE PERSON BEHIND THE WORKINDEPENDENT WORK · LONDON
Rajiv Baskaran.
I build agent software and investigate how human intent becomes reliable, inspectable action.
My current focus is operator interfaces, task orchestration and learning from failure. Physical robot validation is a next step.
Let’s make the next experiment useful.
For conversations about the work, experiments and human–robot interaction.
Payment authorization is one part of accountable agent action.
AgentPay docs