TensorBlue

ROBOTICS
AUTOMATION
INTELLIGENCE

Autonomous manipulation and navigation under uncertainty. RoboHive-powered RL/IL systems for warehouse, manufacturing, and logistics.

SUCCESS_RATE
94.2%
CYCLE_TIME
1.4s
COLLISION_RATE
0.3%
ROBOHIVE
•
ROBOMIMIC
•
BERKELEY_AI
•
META_FAIR
THE_CHALLENGE

Move, pick, and pack millions of SKUs per day

Modern logistics, manufacturing, and retail facilities face high-dimensional, stochastic settings: mixed pallets, uncertain perception, variable friction.

Traditional motion-planning and rule-based control break down.

We solve: autonomous manipulation and navigation under uncertainty, learning control policies that generalize across tasks, layouts, and robot morphologies.

01
High-Dimensional
02
Stochastic
03
Safety-Critical
04
Generalization

Robo
Hive

Berkeley AI Research + Meta AI FAIR
RoboHive Core
Unified environment suite across tasks
Robomimic
Large-scale demonstration learning (BC, BCQ, CQL)
Robosuite
MuJoCo simulation for manipulation
BridgeDataV2
7M+ expert trajectories for IL pretraining
Meta-World
50-task RL benchmark (pick-place, drawer, lever)
SIMULATION
Physics-Accurate
MuJoCo-based sim twin for industrial manipulators and quadrupeds
REAL_HARDWARE
ROS/Isaac Compatible
Unified APIs for policy learning and real robot control stacks
SCALE
Multi-Task Training
50+ tasks across picking, insertion, and bin-packing operations
Pipeline
Sensor → Actuation
1
Sensor Streams
RGB-D, Force, Joint States
2
Perception Backbone
ViT / PointNet / CNN → embeddings
3
RoboHive Environment
MuJoCo sim twin
4
RL Policy
PPO/SAC/DDPG, hybrid BC+RL
5
Sim-to-Real Bridge
Domain rand + noise + delay
6
Hardware Interface
ROS2 / Isaac / Real Robot
7
Monitoring
Episodes, success, reward traces
Observation
Proprioception + vision + force/torque
Action
Joint torques, velocities, or Cartesian goals
Backends
MuJoCo for physics; Isaac/ROS2 for deployment

Task Classes

01
Manipulation & Assembly
EXAMPLES
Bin picking, palletization, screw insertion, cable routing
REWARD
Grasp success + alignment error + torque efficiency
COMPLEXITY
Multi-object reasoning and contact-rich control
02
Mobile Navigation
EXAMPLES
Multi-robot fleet navigation, path planning under uncertainty
REWARD
Negative path length + collision penalty + delivery success
COMPLEXITY
Coordination across multiple agents
03
Human-in-the-Loop
EXAMPLES
Teleoperation data, human demonstrations
REWARD
BC → fine-tune with RL (CQL, SAC)
COMPLEXITY
Leverage BridgeDataV2 with millions of trajectories

Algorithm Stack

RL
SACPPOTD3
Off-policy and on-policy continuous control
IL
BCBCQCQLDiffusion Policy
Leverage human demos to accelerate convergence
Hybrid
H-BCDAPG
BC pretrain + policy gradient fine-tuning
Multi-agent
MAPPOQMIX
Cooperative fleet or dual-arm coordination
Vision-RL
DrQ-v2CURL
RL with high-dimensional visual input
Modular Skill Bank

Pre-trained low-level skills (grasp, move, align) + high-level scheduler for task composition. Enables rapid deployment across diverse warehouse operations.

GRASP
MOVE
ALIGN
PLACE
ROTATE
REWARD_FUNCTION
r_t = (
    + 5.0 * success
    - 1.0 * distance_to_target
    - 0.5 * grasp_slip
    - 0.1 * energy_use
    - 2.0 * collision_flag
)

Sim-to-Real Transfer

5-Step Validation Process
1
Domain Randomization
Textures, lighting, mass, friction
2
Observation Noise
Gaussian/Dropout on sensory channels
3
Dynamics Randomization
Actuator delay, damping, gear ratio
4
Fine-Tuning
Real robot with safe learning rate
5
Transfer Validation
Performance gap <10% vs sim
STATE_REPRESENTATION
Vision (RGB-D)
→
ResNet/ViT encoder
→
Latent (512-D)
Force-Torque (6D)
→
MLP normalization
→
Normalized FT
Proprioceptive
→
Joint angle, velocity
→
Robot state
Concatenated latent → MLP or Transformer policy head

Case Studies

A

Automated Warehouse Pick & Pack

PROBLEM
Thousands of SKU types, variable packaging, mixed lighting
SETUP
6-axis arms with RGB-D + tactile sensors
ENVIRONMENT
RoboHive PickPlace + LiftObject with stochastic friction
POLICY
Vision-based SAC with tactile feedback fusion
RESULTS
Success rate94%
Pick cycle1.4s
Energy reduction−28%
Operator intervention5× reduction
B

Dual-Arm Assembly (Industrial Fixtures)

PROBLEM
Cable insertion and part alignment requiring bimanual coordination
SETUP
Custom RoboHive dual-arm XML with contact sensors
ENVIRONMENT
Multi-agent PPO with communication channel
POLICY
Synchronized grasp/release coordination
RESULTS
Throughput increase+30%
Recovery from perturbation85%
Learning efficiency3× faster
C

Autonomous Mobile Fleet

PROBLEM
Dynamic aisle congestion, battery constraints, collision avoidance
SETUP
Multi-agent RL for routing
ENVIRONMENT
PPO for velocity control + discrete task assignment
POLICY
Decentralized coordination with shared reward
RESULTS
Route length−12%
Idle time−22%
CollisionsNear-zero (100k eps)

MLOps & Metrics

EVALUATION_METRICS
Success Rate
>95%
Completed tasks / total episodes
Cycle Time
<1.2× human
Avg. time per pick/place
Collision Rate
<0.5%
Contacts violating safety zone
Energy Use
−15%
Power consumption vs baseline
Robustness
<5%
Success variance under noise
Generalization
≥90%
Performance on unseen objects
Maintenance
>80%
Autonomous resets without operator
OPERATIONS_STACK
Data Capture
Episode logging
Training
Distributed RL cluster
Validation
Nightly sim tests
Deployment
ROS2 @ 100Hz
Monitoring
Collision heatmaps
ENGINEERING_INSIGHTS
1
Hybrid BC+RL reduces data needs 10× while preserving performance
2
Action masking yields 3× faster convergence by preventing unsafe attempts
3
Curriculum learning (object size + placement variance) enables smooth policy scaling
4
Force feedback closes sim-to-real gap by compensating for contact uncertainty
5
Skill modularity allows policy reuse across pick, place, screw, assemble operations
robot: ur5e
env: PickPlaceCan
sim_backend: mujoco
obs: [proprio, rgb, depth, force]
actions:
  type: joint_velocity
reward:
  success_weight: 5.0
  distance_weight: -1.0
  energy_weight: -0.1
  collision_penalty: -2.0
rl:
  algo: sac
  learning_rate: 3e-4
  buffer_size: 1_000_000
sim2real:
  domain_randomization: true
  noise_intensity: 0.1
  fine_tune_steps: 500_000
deployment:
  control_freq: 100
  safety_guard: true

Autonomous robotics systems.
RoboHive-powered RL/IL.
Production-ready.

SUCCESS_RATE
95%+
CYCLE_TIME
<1.2×
ENERGY_SAVINGS
−28%
Deploy Robot RLSchedule Demo
✓ RoboHive + Robomimic + Robosuite
✓ Berkeley AI Research + Meta FAIR
✓ Sim-to-Real Transfer Validated
✓ ROS2/Isaac Integration
Industrial-grade RL toolkit for manipulation, navigation, and warehouse automation