RoboJEV
uses two-stage Jev `Choice` decisions over structured state to select intent and Cartesian motion/gripper commands for a Franka Panda in MuJoCo, rejecting malformed responses and checking task success independently through physics.
무엇을 판단하는가
CHOICE
Picks one option from a fixed set
SCORE
Rates against ordered levels
NOUL
Answers a yes / no question
Inferred from this project's own one-line summary in the community list — it reads as a CHOICE + NOUL decision. Read the source to confirm.
CHOICE 호출은 이렇게 생겼다
이 질문 유형의 일반적인 뼈대이지 이 프로젝트의 실제 코드가 아니다.
from typesafe import TypeSafe
ts = TypeSafe() # reads TYPESAFE_API_KEY
result = ts.evaluate(
state=page_state,
questions={
"next_action": {
"type": "choice",
"options": ["click", "scroll", "type", "done"],
"instructions": "What should the agent do next?",
}
},
)
action = result["next_action"] # the chosen option
confidence = result["next_action_confidence"]게임과 시뮬레이션 프로젝트 더 보기
typesafe-marioTypeSafe/Jev agent that plays Super Mario Bros. from structured emulator state, choosing each action from emulator-derived features.jev-dronecamera-only autonomous drone in MuJoCo that puts a Jev judgment model in the control loop at 2.5 Hz.tsai-scdrives original StarCraft shareware through keyboard and mouse with Jev action probabilities recorded per decision.jev-plays-pokemonreads Pokémon Red game state as text, answers typed questions each turn, and lets deterministic code turn the answers into moves.