From Code to Steel: NVIDIA ENPIRE Lets AI Agents Conduct Autonomous Research in the Physical World

# Experiment Unit Configuration (Python)
class ExperimentUnit:
    """ENPIRE individual experiment unit configuration"""
    def __init__(self, unit_id: int, ip: str):
        self.unit_id = unit_id
        self.robot_arms = [
            YAMArm(f"{ip}:50051"),  # Arm A
            YAMArm(f"{ip}:50052"),  # Arm B
        ]
        self.camera = RealSenseCamera(f"{ip}:50053")
        self.workstation = GPUWorkstation(
            gpu_model="RTX 5090",
            vram_gb=32,
            local_mode=True
        )
        self.safety = SafetyController(
            joint_limit_deg=270,
            torque_limit_nm=5.0,
            reward_frozen=True
        )
    
    def reset_scene(self) -> bool:
        """Automatic scene reset"""
        self.robot_arms[0].move_to_home()
        self.robot_arms[1].move_to_home()
        return self.camera.verify_scene_ready()

# Environment Auto-Reset Example (Python)
class EnvironmentModule:
    """ENPIRE - Environment Module"""
    def __init__(self, unit: ExperimentUnit):
        self.unit = unit
        self.verifier = VisualVerifier(
            model_path="models/scene_classifier.onnx",
            confidence_threshold=0.95
        )
    
    def auto_reset(self, task_config: dict) -> bool:
        """Automatic scene reset with retry logic"""
        max_retries = 3
        for attempt in range(max_retries):
            # 1. Move all objects to initial positions
            for obj_pose in task_config["initial_poses"]:
                success = self.unit.robot_arms[0].pick_and_place(
                    target_pos=obj_pose["current"],
                    goal_pos=obj_pose["initial"],
                    gripper_force=2.0
                )
                if not success:
                    self._log_error(f"Object {obj_pose['id']} reset failed")
                    continue
            
            # 2. Home the arms
            self.unit.robot_arms[0].move_to_home()
            self.unit.robot_arms[1].move_to_home()
            
            # 3. Visual verification
            frame = self.unit.camera.capture_frame()
            verified, confidence = self.verifier.verify(frame)
            
            if verified and confidence > 0.95:
                return True
            
            self._log_warning(f"Reset attempt {attempt+1} failed, confidence={confidence:.3f}")
        
        return False

# Policy Improvement Example (Python)
class PolicyImprovementModule:
    """ENPIRE - Policy Improvement Module"""
    
    def __init__(self, agent: CodingAgent):
        self.agent = agent
        self.best_policy = None
        self.best_score = -float('inf')
        self.experiment_log = []
    
    def search_and_improve(self, 
                          task_name: str,
                          search_space: dict,
                          max_iterations: int = 10) -> dict:
        """Core policy search and refinement loop"""
        for iteration in range(max_iterations):
            # Agent reads papers, proposes hypothesis
            hypothesis = self.agent.brainstorm(
                task=task_name,
                context=self.experiment_log[-3:],
                search_prompt="Read related papers and propose a new approach"
            )
            
            # Agent autonomously modifies training code
            new_policy_code = self.agent.generate_policy(
                hypothesis=hypothesis,
                base_policy=self.best_policy
            )
            
            compiled = self._compile_and_deploy(new_policy_code)
            
            if compiled:
                rollout_results = self._run_rollout(new_policy_code)
                score = rollout_results["success_rate"]
                
                if score > self.best_score:
                    self.best_policy = new_policy_code
                    self.best_score = score
                    self._git_commit(f"Iter {iteration}: Improved to {score:.2%}")
                
                self.experiment_log.append({
                    "iteration": iteration,
                    "hypothesis": hypothesis,
                    "score": score,
                    "code_hash": hash(new_policy_code)
                })
        
        return {"best_policy": self.best_policy, "best_score": self.best_score}
    
    def _git_commit(self, message: str):
        """Git auto-commit for agent collaboration"""
        import subprocess
        subprocess.run(["git", "add", "."], capture_output=True)
        subprocess.run(["git", "commit", "-m", message], capture_output=True)

# Multi-Robot Parallel Rollout (Python)
import asyncio
from dataclasses import dataclass
from typing import List

@dataclass
class RolloutResult:
    unit_id: int
    success: bool
    execution_time_s: float
    failure_reason: str = ""

class RolloutModule:
    """ENPIRE - Rollout Module"""
    
    def __init__(self, units: List[ExperimentUnit]):
        self.units = units
        self.pass_at_k = 8
    
    async def parallel_rollout(self, 
                             policy_code: str,
                             num_episodes: int = 10) -> dict:
        """Execute parallel policy evaluation across all robots"""
        tasks = []
        for unit in self.units:
            for episode in range(num_episodes // len(self.units)):
                tasks.append(
                    self._run_single_episode(unit, policy_code, episode)
                )
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        successes = [r for r in results if isinstance(r, RolloutResult) and r.success]
        failures = [r for r in results if isinstance(r, RolloutResult) and not r.success]
        
        return {
            "total_episodes": len(results),
            "success_count": len(successes),
            "failure_count": len(failures),
            "success_rate": len(successes) / len(results) * 100,
            "pass_at_8": len(successes) >= self.pass_at_k,
            "avg_execution_time_s": sum(
                r.execution_time_s for r in results 
                if isinstance(r, RolloutResult)
            ) / len(results),
            "failure_analysis": self._analyze_failures(failures)
        }
    
    async def _run_single_episode(self, 
                                 unit: ExperimentUnit,
                                 policy_code: str,
                                 episode_id: int) -> RolloutResult:
        try:
            await asyncio.get_event_loop().run_in_executor(
                None, unit.reset_scene
            )
            start_time = time.time()
            exec_result = await self._execute_policy(unit, policy_code)
            elapsed = time.time() - start_time
            verified = unit.camera.verify_task_complete()
            
            return RolloutResult(
                unit_id=unit.unit_id,
                success=verified,
                execution_time_s=elapsed,
                failure_reason=exec_result.get("error", "") if not verified else ""
            )
        except Exception as e:
            return RolloutResult(
                unit_id=unit.unit_id,
                success=False,
                execution_time_s=0,
                failure_reason=str(e)
            )

// Evolution Module Core - Go Implementation
package evolution

import (
	"context"
	"fmt"
	"log"
	"sync"
	"time"
)

type ExperimentLog struct {
	UnitID      int       `json:"unit_id"`
	Timestamp   time.Time `json:"timestamp"`
	Success     bool      `json:"success"`
	ErrorMsg    string    `json:"error_msg,omitempty"`
	RewardValue float64   `json:"reward_value"`
	DurationMs  int64     `json:"duration_ms"`
}

type FailureAnalyzer struct {
	agent       *CodingAgent
	paperSearch *PaperSearcher
	logStore    []ExperimentLog
	mu          sync.Mutex
}

func (fa *FailureAnalyzer) AnalyzeAndIterate(ctx context.Context, logs []ExperimentLog) (*ImprovementPlan, error) {
	failureCh := make(chan FailurePattern, len(logs))
	var wg sync.WaitGroup
	
	for _, log := range logs {
		if !log.Success {
			wg.Add(1)
			go func(l ExperimentLog) {
				defer wg.Done()
				pattern := fa.analyzeSingleFailure(l)
				failureCh <- pattern
			}(log)
		}
	}
	
	go func() {
		wg.Wait()
		close(failureCh)
	}()
	
	patterns := make([]FailurePattern, 0)
	for p := range failureCh {
		patterns = append(patterns, p)
	}
	
	paperCh := make(chan PaperResult, len(patterns))
	var searchWg sync.WaitGroup
	
	for _, pattern := range patterns {
		searchWg.Add(1)
		go func(p FailurePattern) {
			defer searchWg.Done()
			papers, err := fa.paperSearch.Search(ctx, p.Keyword, 3)
			if err != nil {
				log.Printf("Paper search failed for %s: %v", p.Keyword, err)
				return
			}
			paperCh <- PaperResult{Pattern: p, Papers: papers}
		}(pattern)
	}
	
	go func() {
		searchWg.Wait()
		close(paperCh)
	}()
	
	plan := &ImprovementPlan{
		Timestamp:  time.Now(),
		Changes:    make([]CodeChange, 0),
		Hypothesis: "",
	}
	
	for pr := range paperCh {
		if len(pr.Papers) > 0 {
			change := fa.agent.ProposeChange(pr.Pattern, pr.Papers[0])
			plan.Changes = append(plan.Changes, change)
		}
	}
	
	plan.Hypothesis = fa.agent.SynthesizeHypothesis(patterns, plan.Changes)
	return plan, nil
}

type ImprovementPlan struct {
	Timestamp  time.Time    `json:"timestamp"`
	Changes    []CodeChange `json:"changes"`
	Hypothesis string       `json:"hypothesis"`
}

type CodeChange struct {
	File        string `json:"file"`
	Description string `json:"description"`
	Code        string `json:"code"`
}

func (plan *ImprovementPlan) ApplyToRepository(repo *GitRepository) error {
	for _, change := range plan.Changes {
		if err := repo.ApplyChange(change); err != nil {
			return fmt.Errorf("apply change failed: %w", err)
		}
	}
	return repo.Commit(plan.Hypothesis)
}

// Agent Scheduling & Load Balancing - Go Concurrency Model
package scheduler

import (
	"context"
	"log"
	"sync"
	"time"
)

type AgentTask struct {
	ID          string                 `json:"id"`
	AgentType   string                 `json:"agent_type"`
	UnitID      int                    `json:"unit_id"`
	TaskType    string                 `json:"task_type"`
	Config      map[string]interface{} `json:"config"`
	CreatedAt   time.Time              `json:"created_at"`
	MaxDuration time.Duration          `json:"max_duration"`
}

type Scheduler struct {
	agents    map[string]*AgentHandle
	units     []*ExperimentUnit
	workQueue chan AgentTask
	results   sync.Map
}

func NewScheduler(agentConfigs map[string]AgentConfig, units []*ExperimentUnit) *Scheduler {
	s := &Scheduler{
		agents:    make(map[string]*AgentHandle),
		units:     units,
		workQueue: make(chan AgentTask, 100),
	}
	for agentType, config := range agentConfigs {
		for i := 0; i < config.Instances; i++ {
			handle := &AgentHandle{
				ID:        fmt.Sprintf("%s-%d", agentType, i),
				AgentType: agentType,
				Config:    config,
				Busy:      false,
			}
			s.agents[handle.ID] = handle
		}
	}
	return s
}

func (s *Scheduler) Start(ctx context.Context) {
	var wg sync.WaitGroup
	for i := 0; i < len(s.agents); i++ {
		wg.Add(1)
		go func() {
			defer wg.Done()
			for {
				select {
				case task := <-s.workQueue:
					s.executeTask(ctx, task)
				case <-ctx.Done():
					return
				}
			}
		}()
	}
	wg.Wait()
}

func (s *Scheduler) executeTask(ctx context.Context, task AgentTask) {
	agent := s.findAvailableAgent(task.AgentType)
	if agent == nil {
		log.Printf("No available agent for task %s", task.ID)
		return
	}
	agent.Busy = true
	defer func() { agent.Busy = false }()
	
	unit := s.units[task.UnitID]
	startTime := time.Now()
	result := agent.Run(ctx, unit, task)
	
	s.results.Store(task.ID, TaskResult{
		TaskID:     task.ID,
		AgentID:    agent.ID,
		UnitID:     task.UnitID,
		Success:    result.Success,
		Duration:   time.Since(startTime),
		TokenUsage: result.TokenUsage,
	})
	
	if result.Success {
		gitShare(agent.ID, task.ID, result.PolicyCode)
	}
}

// Physical Scaling Law Simulation - Go Concurrency Model
package scaling

import (
	"fmt"
	"math"
	"sync"
	"time"
)

type ScalingExperiment struct {
	NumRobots      int
	NumAgents      int
	AgentThinkTime time.Duration
	RobotExecTime  time.Duration
	CommOverhead   time.Duration
}

type SimulateResult struct {
	NumRobots        int
	TotalTime        time.Duration
	Speedup          float64
	MRU              float64
	TokenConsumption int64
}

func SimulatePhysicalScaling(configs []ScalingExperiment) []SimulateResult {
	results := make([]SimulateResult, len(configs))
	var wg sync.WaitGroup
	for i, config := range configs {
		wg.Add(1)
		go func(idx int, cfg ScalingExperiment) {
			defer wg.Done()
			results[idx] = runSimulation(cfg)
		}(i, config)
	}
	wg.Wait()
	return results
}

func runSimulation(cfg ScalingExperiment) SimulateResult {
	baseTime := 90.0 * float64(time.Minute)
	alpha := 0.15
	N := float64(cfg.NumRobots)
	actualSpeedup := N / (1 + alpha*(N-1))
	totalTime := baseTime / actualSpeedup
	
	totalCycleTime := float64(cfg.RobotExecTime) + 
		float64(cfg.AgentThinkTime) + 
		float64(cfg.CommOverhead)*(N-1)
	mru := float64(cfg.RobotExecTime) / totalCycleTime
	
	tokenBase := int64(100000)
	tokenConsumption := tokenBase * int64(N) * int64(1+int(0.3*(N-1)))
	
	return SimulateResult{
		NumRobots:        cfg.NumRobots,
		TotalTime:        time.Duration(totalTime),
		Speedup:          actualSpeedup,
		MRU:              math.Min(mru, 1.0),
		TokenConsumption: tokenConsumption,
	}
}

func RunBenchmark() {
	configs := []ScalingExperiment{
		{NumRobots: 1, NumAgents: 1, AgentThinkTime: 30 * time.Second, 
		 RobotExecTime: 25 * time.Second, CommOverhead: 5 * time.Second},
		{NumRobots: 4, NumAgents: 4, AgentThinkTime: 35 * time.Second, 
		 RobotExecTime: 25 * time.Second, CommOverhead: 8 * time.Second},
		{NumRobots: 8, NumAgents: 8, AgentThinkTime: 40 * time.Second, 
		 RobotExecTime: 25 * time.Second, CommOverhead: 12 * time.Second},
	}
	
	results := SimulatePhysicalScaling(configs)
	fmt.Println("=== Physical Scaling Law Benchmark ===")
	fmt.Printf("%-12s %-16s %-12s %-12s %-16s\n", 
		"Robots", "Total Time", "Speedup", "MRU", "Token Cons.")
	for _, r := range results {
		fmt.Printf("%-12d %-16s %-12.2f %-12.2f %-16d\n",
			r.NumRobots, r.TotalTime.Round(time.Second), r.Speedup, r.MRU, r.TokenConsumption)
	}
}

# Robot Control Interface Example (Python)
import numpy as np
from typing import Tuple

class RobotControlInterface:
    """ENPIRE robot control API - directly callable by agents"""
    
    def __init__(self, grpc_endpoint: str):
        self.endpoint = grpc_endpoint
        self._connect()
    
    def move_to_pose(self, x, y, z, roll, pitch, yaw, speed=0.1) -> bool:
        return self._call_grpc("MoveToPose", {
            "pose": [x, y, z, roll, pitch, yaw], "speed": speed
        })
    
    def grasp(self, force: float = 2.0) -> bool:
        return self._call_grpc("Grasp", {"force": force})
    
    def release(self) -> bool:
        return self._call_grpc("Release", {})
    
    def apply_force(self, force_vector: Tuple[float, float, float], duration: float = 1.0) -> bool:
        return self._call_grpc("ApplyForce", {
            "force": list(force_vector), "duration": duration
        })
    
    def get_joint_states(self) -> np.ndarray:
        response = self._call_grpc("GetJointStates", {})
        return np.array(response["joint_angles"])
    
    def _call_grpc(self, method: str, params: dict) -> dict:
        return {"status": "ok", "data": {}}

class SafetyController:
    """ENPIRE dual-layer safety system"""
    def __init__(self, joint_soft_limit_deg=270.0, joint_hard_limit_deg=300.0,
                 torque_limit_nm=5.0, force_limit_n=20.0):
        self.soft_limit = joint_soft_limit_deg
        self.hard_limit = joint_hard_limit_deg
        self.emergency_stopped = False
    
    def check_motion_safety(self, target_joints: np.ndarray) -> bool:
        if self.emergency_stopped:
            return False
        for i, angle in enumerate(np.degrees(target_joints)):
            if abs(angle) > self.soft_limit:
                if abs(angle) > self.hard_limit:
                    self.emergency_stop(f"Joint {i} exceeded hard limit: {angle:.1f}deg")
                    return False
        return True
    
    def emergency_stop(self, reason: str):
        self.emergency_stopped = True
        self._cut_power()
        print(f"[SAFETY] EMERGENCY STOP: {reason}")
    
    def _cut_power(self):
        pass