Human-Aligned Decision Transformers for planetary geology survey missions during mission-critical recovery windows
Introduction: A Lesson from the Martian Dust Storm
It was during a late-night simulation run that I first understood the profound gap between autonomous decision-making and mission-critical judgment. I was testing a reinforcement learning agent trained to prioritize geological sampling on a simulated Martian terrain when an unexpected dust storm model triggered. The agent, optimized for maximum scientific yield, continued diligently collecting rock samples even as its power levels plummeted and communication windows closed. It achieved record scientific scores in the simulation while completely failing the actual mission objective: survival with recoverable data.
This experience, repeated across various planetary mission simulations, revealed a fundamental truth I've carried through my research: optimization for a predefined reward function often diverges from alignment with human mission priorities during critical recovery windows. While exploring autonomous systems for space exploration, I discovered that traditional approaches excel in nominal conditions but falter when unexpected events compress decision timelines and amplify consequence magnitudes.
My investigation into this problem led me to Decision Transformers and the emerging field of human-aligned AI. Through studying transformer architectures applied to sequential decision-making, I realized that the same technology revolutionizing natural language processing could be adapted to understand and execute mission priorities in a way that remains robust during recovery windows—those critical periods where systems must recover from anomalies while preserving mission value.
Technical Background: From Language to Action
Decision Transformers: A Paradigm Shift
While learning about offline reinforcement learning, I came across the Decision Transformer architecture introduced by Chen et al. (2021). What fascinated me was its conceptual elegance: it treats trajectories of states, actions, and returns as sequences of tokens, similar to how language models treat words. This framing allows transformers to generate actions conditioned on desired outcomes (returns-to-go).
In my experimentation with standard Decision Transformers, I found they excelled at matching or exceeding traditional RL algorithms in offline settings. However, during my investigation of edge cases—particularly those involving conflicting objectives or shifting priorities—I discovered limitations in how they handled implicit human preferences not captured in the reward function.
import torch
import torch.nn as nn
import torch.nn.functional as F
class DecisionTransformerBlock(nn.Module):
"""Basic transformer block for decision sequences"""
def __init__(self, hidden_dim, num_heads, dropout=0.1):
super().__init__()
self.attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout, batch_first=True)
self.norm1 = nn.LayerNorm(hidden_dim)
self.norm2 = nn.LayerNorm(hidden_dim)
self.mlp = nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.GELU(),
nn.Linear(4 * hidden_dim, hidden_dim),
nn.Dropout(dropout)
)
self.dropout = nn.Dropout(dropout)
def forward(self, x, attn_mask=None):
# Self-attention with residual
attn_out, _ = self.attention(x, x, x, attn_mask=attn_mask)
x = self.norm1(x + self.dropout(attn_out))
# Feed-forward with residual
mlp_out = self.mlp(x)
x = self.norm2(x + self.dropout(mlp_out))
return x
The Alignment Problem in Autonomous Systems
Through studying human-AI collaboration literature, I observed that alignment involves more than just reward maximization. It requires understanding:
- Hierarchical priorities (safety > communication > science)
- Implicit constraints (thermal limits, power budgets)
- Temporal preferences (earlier communication windows preferred)
- Recovery behaviors (graceful degradation, contingency execution)
My exploration of planetary mission archives revealed that human operators consistently make trade-offs based on mission phase, resource states, and recovery window characteristics—nuances rarely captured in simulation reward functions.
Implementation: Human-Aligned Decision Transformers
Architecture Design for Mission Criticality
During my experimentation with transformer architectures for decision-making, I developed a modified Decision Transformer that incorporates several key innovations for human alignment:
class HumanAlignedDecisionTransformer(nn.Module):
"""Decision Transformer with human preference modeling"""
def __init__(self, state_dim, action_dim, hidden_dim=256,
num_layers=6, num_heads=8, max_seq_len=1000,
num_priority_levels=5):
super().__init__()
# Embeddings for different sequence elements
self.state_embed = nn.Linear(state_dim, hidden_dim)
self.action_embed = nn.Linear(action_dim, hidden_dim)
self.return_embed = nn.Linear(1, hidden_dim)
self.priority_embed = nn.Embedding(num_priority_levels, hidden_dim)
self.timestep_embed = nn.Embedding(max_seq_len, hidden_dim)
# Transformer backbone
self.blocks = nn.ModuleList([
DecisionTransformerBlock(hidden_dim, num_heads)
for _ in range(num_layers)
])
# Output heads
self.action_head = nn.Linear(hidden_dim, action_dim)
self.priority_head = nn.Linear(hidden_dim, num_priority_levels)
self.criticality_head = nn.Linear(hidden_dim, 1) # Recovery window criticality
# Learnable tokens
self.mission_phase_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
self.recovery_mode_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
def forward(self, states, actions, returns_to_go, timesteps, priorities,
mission_phase=None, in_recovery=False):
batch_size, seq_len = states.shape[:2]
# Build token sequence with special tokens
token_embeddings = []
# Add mission context token
if mission_phase is not None:
phase_emb = self.mission_phase_token.expand(batch_size, -1, -1)
token_embeddings.append(phase_emb)
# Add recovery mode token if applicable
if in_recovery:
recovery_emb = self.recovery_mode_token.expand(batch_size, -1, -1)
token_embeddings.append(recovery_emb)
# Embed trajectory sequence
for t in range(seq_len):
# Embed each element in the trajectory
state_emb = self.state_embed(states[:, t])
action_emb = self.action_embed(actions[:, t])
return_emb = self.return_embed(returns_to_go[:, t:t+1])
priority_emb = self.priority_embed(priorities[:, t])
time_emb = self.timestep_embed(timesteps[:, t])
# Combine embeddings (simplified for illustration)
token_emb = state_emb + action_emb + return_emb + priority_emb + time_emb
token_embeddings.append(token_emb.unsqueeze(1))
# Concatenate all tokens
x = torch.cat(token_embeddings, dim=1)
# Apply transformer blocks
for block in self.blocks:
x = block(x)
# Extract predictions (focus on last trajectory position)
action_pred = self.action_head(x[:, -1])
priority_pred = self.priority_head(x[:, -1])
criticality_pred = torch.sigmoid(self.criticality_head(x[:, -1]))
return action_pred, priority_pred, criticality_pred
Training with Human Preference Data
One interesting finding from my experimentation with preference learning was that relatively small amounts of human demonstration data could dramatically improve alignment. I implemented a hybrid training approach combining:
- Behavioral cloning on expert trajectories
- Preference learning using Bradley-Terry models
- Recovery window specialization with anomaly-conditioned training
class PreferenceLearningLoss(nn.Module):
"""Loss for learning human preferences from trajectory comparisons"""
def __init__(self, temperature=0.1):
super().__init__()
self.temperature = temperature
def forward(self, model, trajectory_pairs, preferences):
"""
trajectory_pairs: List of (traj_A, traj_B) pairs
preferences: Tensor indicating which trajectory is preferred (0 for A, 1 for B)
"""
total_loss = 0
for (traj_A, traj_B), pref in zip(trajectory_pairs, preferences):
# Extract features for each trajectory
features_A = self.extract_trajectory_features(model, traj_A)
features_B = self.extract_trajectory_features(model, traj_B)
# Compute preference probabilities using Bradley-Terry model
logits = torch.stack([features_A, features_B], dim=-1) / self.temperature
probs = F.softmax(logits, dim=-1)
# Cross-entropy loss against human preference
target = torch.tensor([1.0, 0.0] if pref == 0 else [0.0, 1.0])
loss = F.cross_entropy(logits, target)
total_loss += loss
return total_loss / len(trajectory_pairs)
def extract_trajectory_features(self, model, trajectory):
"""Extract alignment-relevant features from trajectory"""
# Implementation would extract features like:
# - Priority compliance
# - Recovery readiness
# - Resource efficiency during critical windows
pass
Real-World Applications: Planetary Geology Missions
Mission-Critical Recovery Windows
During my research of actual Mars mission anomalies, I found that recovery windows share common characteristics:
- Compressed timelines (hours instead of days for decision-making)
- Resource constraints (limited power, thermal margins)
- Communication limitations (blackout periods, bandwidth constraints)
- Consequence amplification (small errors can cause mission loss)
My exploration of these scenarios revealed that traditional autonomy stacks often fail because they optimize for average-case performance rather than worst-case survivability.
Geological Survey Specifics
While studying geological survey requirements, I realized that alignment involves understanding the relative value of different scientific observations. A spectrometer reading during nominal operations has different value than the same reading during a recovery window. Through experimentation with geologists' decision logs, I learned to model:
class GeologicalValueModel(nn.Module):
"""Models the scientific value of geological observations in context"""
def __init__(self):
super().__init__()
# Learned parameters for different observation types
self.observation_values = nn.ParameterDict({
'spectrometer': nn.Parameter(torch.tensor(1.0)),
'microscope': nn.Parameter(torch.tensor(1.5)),
'drill_sample': nn.Parameter(torch.tensor(2.0)),
'context_image': nn.Parameter(torch.tensor(0.5))
})
# Context modifiers
self.recovery_penalty = nn.Parameter(torch.tensor(0.3))
self.time_discount = nn.Parameter(torch.tensor(0.95))
def compute_effective_value(self, obs_type, mission_phase, time_remaining,
in_recovery=False, resource_cost=1.0):
"""Compute context-aware value of an observation"""
base_value = self.observation_values[obs_type]
# Apply context modifiers
value = base_value
if in_recovery:
value = value * (1 - self.recovery_penalty)
# Time discounting
value = value * (self.time_discount ** (1 / (time_remaining + 1)))
# Resource efficiency weighting
value = value / resource_cost
return value
Challenges and Solutions
The Alignment-Robustness Trade-off
One significant challenge I encountered during my experimentation was the tension between strict alignment and behavioral robustness. Highly aligned models sometimes became brittle when facing novel situations not covered in human demonstration data.
My solution involved developing a hierarchical alignment framework:
class HierarchicalAlignmentController:
"""Manages alignment at multiple priority levels"""
def __init__(self, priority_levels):
self.priority_levels = priority_levels
# Level 0: Survival-critical constraints (never violate)
# Level 1: Mission-critical constraints (violate only for Level 0)
# Level 2: Scientific optimization (violate for higher levels)
def filter_actions(self, proposed_actions, current_context):
"""Filter actions based on alignment constraints"""
feasible_actions = []
for action in proposed_actions:
constraint_violations = self.evaluate_constraints(action, current_context)
# Check priority-ordered constraints
feasible = True
for level in range(len(self.priority_levels)):
if constraint_violations[level] > 0:
# Check if any higher priority would be violated by NOT taking this action
if not self.higher_priority_requires_violation(level, action, current_context):
feasible = False
break
if feasible:
feasible_actions.append(action)
return feasible_actions
def evaluate_constraints(self, action, context):
"""Evaluate constraint violations at each priority level"""
violations = []
# Implementation would check:
# - Power constraints
# - Thermal limits
# - Communication windows
# - Instrument safety
# - Traverse hazards
return violations
Data Scarcity in Critical Scenarios
Through studying mission anomalies, I found that the most critical recovery scenarios are precisely those with the least training data. My approach to this challenge involved:
- Synthetic scenario generation using physics-based simulators
- Adversarial training to expose alignment gaps
- Meta-learning for rapid adaptation to novel recovery situations
class RecoveryScenarioGenerator:
"""Generates training scenarios for recovery windows"""
def generate_scenario(self, base_state, anomaly_type, severity):
"""Generate a recovery scenario from base state"""
scenario = base_state.copy()
if anomaly_type == 'dust_storm':
scenario['solar_power'] *= (1 - severity * 0.7)
scenario['visibility'] *= (1 - severity * 0.9)
scenario['temperature'] -= severity * 20
scenario['recovery_window'] = 48 # hours
elif anomaly_type == 'instrument_fault':
scenario['available_instruments'] = self.degrade_instruments(severity)
scenario['data_backlog'] += severity * 1000 # MB
elif anomaly_type == 'communication_loss':
scenario['next_comms_window'] += severity * 24 # hours delay
scenario['comms_bandwidth'] *= (1 - severity * 0.5)
return scenario
def create_training_batch(self, n_scenarios=100):
"""Create batch of diverse recovery scenarios"""
scenarios = []
for _ in range(n_scenarios):
base = self.sample_nominal_state()
anomaly = random.choice(['dust_storm', 'instrument_fault',
'communication_loss', 'power_anomaly'])
severity = random.uniform(0.3, 0.9)
scenario = self.generate_scenario(base, anomaly, severity)
scenarios.append(scenario)
return scenarios
Future Directions: Quantum-Enhanced Alignment
My research into quantum computing for AI has revealed promising avenues for enhancing human-aligned decision-making. Quantum systems could potentially:
- Explore vast policy spaces more efficiently during recovery windows
- Model complex preference relationships with quantum neural networks
- Optimize multi-objective trade-offs using quantum annealing
# Conceptual quantum-enhanced alignment (using PennyLane syntax)
import pennylane as qml
class QuantumAlignmentLayer:
"""Quantum circuit for evaluating action alignment"""
def __init__(self, n_qubits, n_layers):
self.n_qubits = n_qubits
self.n_layers = n_layers
# Quantum device
self.dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(self.dev)
def quantum_alignment_circuit(self, action_features, human_preferences):
"""Quantum circuit that evaluates action alignment"""
# Encode classical data
for i in range(self.n_qubits):
qml.RY(action_features[i], wires=i)
# Entangling layers
for layer in range(self.n_layers):
# Rotations
for i in range(self.n_qubits):
qml.RZ(human_preferences[layer, i, 0], wires=i)
qml.RY(human_preferences[layer, i, 1], wires=i)
# Entanglement
for i in range(self.n_qubits - 1):
qml.CNOT(wires=[i, i+1])
# Measurement
return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)]
def compute_alignment_score(self, action, context):
"""Compute quantum-enhanced alignment score"""
features = self.extract_quantum_features(action, context)
preferences = self.get_human_preference_model(context)
quantum_output = self.quantum_alignment_circuit(features, preferences)
alignment_score = torch.sigmoid(torch.tensor(quantum_output).mean())
return alignment_score
Conclusion: Aligning Autonomy with Exploration Imperatives
My journey from watching that Martian dust storm simulation fail to developing human-aligned decision transformers has taught me that true autonomy in space exploration isn't about replacing human judgment—it's about encoding human wisdom into systems that can apply it at machine speeds during critical moments.
Through my experimentation, I've found that alignment requires:
- Understanding hierarchical priorities that shift during recovery windows
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