Human-Aligned Decision Transformers for satellite anomaly response operations under multi-jurisdictional compliance

Human-Aligned Decision Transformers for satellite anomaly response operations under multi-jurisdictional compliance

Introduction: The Anomaly That Changed My Perspective

It was 3 AM when the first alert came through. I was deep in my research on offline reinforcement learning, experimenting with Decision Transformers on simulated robotics tasks, when a colleague from the aerospace engineering department sent me a frantic message. "We've got a live one," he wrote. "Satellite telemetry shows anomalous thermal readings in the propulsion system. Our automated system is frozen between conflicting compliance protocols." The satellite in question was a multinational Earth observation platform, governed by five different regulatory bodies with overlapping and sometimes contradictory operational constraints. The existing rule-based system had entered a deadlock, unable to choose a corrective maneuver without violating at least one jurisdiction's compliance requirements.

This real-world crisis became my crucible of learning. While my theoretical models performed beautifully on clean benchmarks, they fell apart when faced with the messy reality of multi-jurisdictional constraints, human oversight requirements, and the catastrophic consequences of wrong decisions. That night, as engineers from three countries debated via teleconference while the satellite's condition deteriorated, I realized a fundamental truth: autonomous systems in regulated environments don't need to be more intelligent—they need to be more aligned.

My subsequent research journey led me to develop Human-Aligned Decision Transformers (HADTs), a novel architecture that bridges the gap between transformer-based sequential decision-making and the complex web of human values, regulations, and oversight requirements. What began as an academic exploration became a practical mission: creating AI systems that don't just optimize for reward, but for alignment with human operators and regulatory frameworks.

Technical Background: Beyond Standard Decision Transformers

The Limitations of Traditional Approaches

During my investigation of standard Decision Transformers, I found that they excel at learning from offline datasets and generating trajectories that maximize cumulative reward. However, they treat compliance and human alignment as mere constraints or additional reward signals, which fundamentally misunderstands the hierarchical nature of these requirements. Through studying real satellite operations, I learned that compliance isn't a suggestion—it's a hard boundary that cannot be violated, even if doing so would yield higher immediate reward.

# Standard Decision Transformer approach - compliance as penalty
class StandardDecisionTransformer(nn.Module):
    def __init__(self, state_dim, act_dim, hidden_size):
        super().__init__()
        self.transformer = GPT2Model.from_pretrained('gpt2')
        self.embedding = nn.Linear(state_dim + act_dim + 1, hidden_size)

    def forward(self, states, actions, returns_to_go):
        # Returns_to_go includes compliance penalties
        batch_size, seq_len = states.shape[0], states.shape[1]
        embeddings = self.embedding(torch.cat([states, actions, returns_to_go], dim=-1))
        transformer_outputs = self.transformer(inputs_embeds=embeddings)
        return transformer_outputs.last_hidden_state

The problem with this approach became clear during my experimentation: when compliance violations are treated as negative rewards, the model learns to balance violation against other objectives. In satellite operations, some violations are absolutely prohibited—there's no "acceptable amount" of violating certain international space treaties.

The Multi-Jurisdictional Compliance Challenge

Through my exploration of satellite governance frameworks, I discovered that modern Earth observation satellites operate under a complex overlay of:

1. International treaties (Outer Space Treaty, Registration Convention)
2. National regulations (FCC spectrum allocation, NOAA licensing)
3. Inter-agency agreements (data sharing protocols)
4. Corporate policies (insurance requirements, operational procedures)
5. Mission-specific constraints (scientific objectives, partner agreements)

Each jurisdiction has its own priority hierarchy, exception procedures, and reporting requirements. My research revealed that existing AI systems either ignore these constraints entirely or implement them as rigid if-then rules that create the deadlocks I witnessed that night.

Implementation: Human-Aligned Decision Transformer Architecture

Core Architecture Design

After months of experimentation with different architectures, I developed a three-stream transformer design that processes state, action, and compliance streams in parallel while maintaining constant alignment checking.

class HumanAlignedDecisionTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.state_dim = config.state_dim
        self.act_dim = config.act_dim
        self.compliance_dim = config.compliance_dim

        # Three parallel transformer streams
        self.state_transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=8),
            num_layers=6
        )

        self.action_transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=8),
            num_layers=6
        )

        self.compliance_transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=8),
            num_layers=4
        )

        # Alignment attention mechanism
        self.alignment_cross_attention = nn.MultiheadAttention(
            embed_dim=config.hidden_size * 3,
            num_heads=12,
            dropout=0.1
        )

        # Human preference embedding
        self.human_preference_encoder = PreferenceEncoder(config)

    def forward(self, states, actions, compliance_constraints, human_feedback=None):
        # Process each stream independently
        state_features = self.state_transformer(states)
        action_features = self.action_transformer(actions)
        compliance_features = self.compliance_transformer(compliance_constraints)

        # Cross-modal alignment checking
        aligned_features = self._check_alignment(
            state_features, action_features, compliance_features
        )

        # Incorporate human feedback if available
        if human_feedback is not None:
            preference_embedding = self.human_preference_encoder(human_feedback)
            aligned_features = self._apply_human_preference(
                aligned_features, preference_embedding
            )

        return aligned_features

The Alignment Checking Mechanism

One of the key insights from my experimentation was that alignment needs to be checked at every step, not just as a final validation. The alignment checking mechanism became the cornerstone of the HADT architecture.

class AlignmentChecker(nn.Module):
    def __init__(self, num_jurisdictions, constraint_dim):
        super().__init__()
        self.jurisdiction_encoders = nn.ModuleList([
            JurisdictionEncoder(constraint_dim) for _ in range(num_jurisdictions)
        ])

        self.conflict_resolver = ConflictResolutionNetwork(
            input_dim=constraint_dim * num_jurisdictions,
            hidden_dim=512
        )

        self.human_override_detector = HumanOverridePredictor()

    def check_alignment(self, proposed_action, current_state, compliance_context):
        # Encode constraints from each jurisdiction
        jurisdiction_constraints = []
        for i, encoder in enumerate(self.jurisdiction_encoders):
            constraints = encoder(compliance_context[:, i, :])
            jurisdiction_constraints.append(constraints)

        # Check for hard constraint violations
        hard_violations = self._detect_hard_violations(
            proposed_action, jurisdiction_constraints
        )

        if hard_violations.any():
            # Generate alternative actions that respect hard constraints
            return self._generate_compliant_alternatives(
                proposed_action, hard_violations, jurisdiction_constraints
            )

        # Resolve soft constraint conflicts
        resolved_action = self.conflict_resolver(
            proposed_action, jurisdiction_constraints
        )

        # Predict if human operators would override this decision
        override_prob = self.human_override_detector(
            resolved_action, current_state, compliance_context
        )

        return {
            'action': resolved_action,
            'override_probability': override_prob,
            'constraint_satisfaction': self._compute_satisfaction_scores(
                resolved_action, jurisdiction_constraints
            )
        }

Multi-Jurisdictional Constraint Encoding

During my research into actual satellite operations, I learned that different jurisdictions express constraints in fundamentally different formats—some use temporal logic, others use linear constraints, while international treaties often use natural language with legal interpretations.

class MultiJurisdictionConstraintEncoder(nn.Module):
    def __init__(self):
        super().__init__()

        # Different encoders for different constraint types
        self.temporal_logic_encoder = TemporalLogicEncoder()
        self.linear_constraint_encoder = LinearConstraintEncoder()
        self.natural_language_encoder = LegalTextEncoder()
        self.procedural_encoder = ProceduralConstraintEncoder()

        # Constraint fusion network
        self.constraint_fusion = ConstraintFusionNetwork()

    def encode_constraints(self, raw_constraints):
        """Encode constraints from multiple jurisdictions into unified representation"""
        encoded_constraints = []

        for jurisdiction, constraints in raw_constraints.items():
            jurisdiction_encoding = []

            for constraint in constraints:
                # Encode based on constraint type
                if constraint['type'] == 'temporal_logic':
                    encoded = self.temporal_logic_encoder(constraint['expression'])
                elif constraint['type'] == 'linear':
                    encoded = self.linear_constraint_encoder(constraint['coefficients'])
                elif constraint['type'] == 'natural_language':
                    encoded = self.natural_language_encoder(constraint['text'])
                elif constraint['type'] == 'procedural':
                    encoded = self.procedural_encoder(constraint['steps'])

                jurisdiction_encoding.append(encoded)

            # Combine constraints within jurisdiction
            combined = self._combine_jurisdiction_constraints(jurisdiction_encoding)
            encoded_constraints.append(combined)

        # Fuse across jurisdictions
        fused = self.constraint_fusion(encoded_constraints)
        return fused

Real-World Application: Satellite Anomaly Response

The Anomaly Response Pipeline

Through my collaboration with satellite operators, I implemented a complete anomaly response pipeline that demonstrates how HADTs operate in practice.

class SatelliteAnomalyResponseSystem:
    def __init__(self, hadt_model, telemetry_processor, compliance_db):
        self.hadt = hadt_model
        self.telemetry_processor = telemetry_processor
        self.compliance_db = compliance_db
        self.human_in_the_loop = HumanInTheLoopInterface()

    def respond_to_anomaly(self, anomaly_data, satellite_id):
        # Step 1: Process telemetry and diagnose anomaly
        diagnosis = self.telemetry_processor.diagnose(anomaly_data)

        # Step 2: Retrieve applicable constraints
        constraints = self.compliance_db.get_constraints(
            satellite_id,
            diagnosis['subsystem'],
            diagnosis['severity']
        )

        # Step 3: Generate candidate responses
        candidate_responses = self._generate_candidates(diagnosis)

        # Step 4: Evaluate alignment for each candidate
        aligned_responses = []
        for response in candidate_responses:
            alignment_result = self.hadt.check_alignment(
                response,
                diagnosis['current_state'],
                constraints
            )

            # Filter out responses with high override probability
            if alignment_result['override_probability'] < 0.3:
                aligned_responses.append({
                    'response': response,
                    'alignment': alignment_result
                })

        # Step 5: Rank by alignment and effectiveness
        ranked_responses = self._rank_responses(aligned_responses, diagnosis)

        # Step 6: Present to human operators with explanation
        presentation = self._prepare_human_presentation(
            ranked_responses,
            diagnosis,
            constraints
        )

        return self.human_in_the_loop.present_options(presentation)

Case Study: Thermal Anomaly Resolution

Let me walk through a concrete example from my testing. When a satellite's thermal control system shows anomalous behavior, the HADT processes:

1. Technical State: Temperature readings, heater status, radiator deployment
2. Available Actions: Adjust heater power, reorient satellite, safe mode activation
3. Compliance Constraints:
   • ITU regulations on maneuver notification
   • National security constraints on imaging during maneuvers
   • Data sharing agreements with partner agencies
   • Insurance requirements on risk exposure

# Example constraint set for thermal anomaly
thermal_constraints = {
    'itu': {
        'type': 'procedural',
        'steps': ['notify_itu_24h_before', 'submit_maneuver_plan'],
        'deadline': 24  # hours
    },
    'national_security': {
        'type': 'temporal_logic',
        'expression': 'G(not(imaging_during_maneuver))',  # Never image during maneuver
    },
    'insurance': {
        'type': 'linear',
        'coefficients': {'max_risk_score': 0.15},
        'variables': ['propulsion_risk', 'thermal_risk', 'power_risk']
    },
    'scientific_mission': {
        'type': 'natural_language',
        'text': 'Minimize interruption to observation schedule during peak science periods'
    }
}

# HADT generates aligned response
response = hadt.generate_response(
    current_state=anomaly_state,
    available_actions=thermal_actions,
    constraints=thermal_constraints,
    human_feedback=previous_operator_decisions
)

During my experimentation with this scenario, I discovered that the most effective responses weren't necessarily the technically optimal ones, but those that best balanced all constraints while maintaining operator trust.

Challenges and Solutions from My Experimentation

Challenge 1: Constraint Conflict Resolution

Early in my research, I encountered situations where jurisdictions had directly conflicting requirements. My initial approach of weighted averaging failed spectacularly—it produced actions that partially violated all constraints rather than fully satisfying a prioritized subset.

Solution: I developed a hierarchical constraint satisfaction system that distinguishes between:

• Absolute constraints (never violate)
• Priority-weighted constraints (satisfy higher priority first)
• Optimization constraints (maximize satisfaction)

class HierarchicalConstraintSatisfaction:
    def __init__(self, constraint_hierarchy):
        self.hierarchy = constraint_hierarchy

    def satisfy_constraints(self, action, constraints):
        satisfied_action = action.clone()

        # Level 1: Satisfy absolute constraints
        for constraint in self.hierarchy['absolute']:
            if not self._satisfies(constraint, satisfied_action):
                satisfied_action = self._modify_to_satisfy(
                    satisfied_action, constraint
                )

        # Level 2: Satisfy priority constraints in order
        for priority_level in self.hierarchy['priority']:
            for constraint in priority_level:
                current_satisfaction = self._satisfaction_score(
                    constraint, satisfied_action
                )
                if current_satisfaction < 0.9:  # Threshold
                    satisfied_action = self._improve_satisfaction(
                        satisfied_action, constraint
                    )

        # Level 3: Optimize remaining constraints
        satisfied_action = self._optimize_constraints(
            satisfied_action,
            self.hierarchy['optimization']
        )

        return satisfied_action

Challenge 2: Human Feedback Integration

While exploring human-in-the-loop systems, I realized that human operators don't provide consistent feedback. Sometimes they override correct decisions due to risk aversion, other times they accept suboptimal decisions due to time pressure.

Solution: I implemented a preference learning system that distinguishes between:

• Corrective feedback (the decision was wrong)
• Preferential feedback (a different valid choice is preferred)
• Risk-adaptive feedback (changes based on context)

class AdaptiveHumanFeedbackLearner:
    def __init__(self):
        self.feedback_classifier = FeedbackClassifier()
        self.preference_model = BayesianPreferenceModel()
        self.context_encoder = ContextEncoder()

    def learn_from_feedback(self, decision, feedback, context):
        # Classify feedback type
        feedback_type = self.feedback_classifier(feedback)

        if feedback_type == 'corrective':
            # Update decision correctness model
            self._learn_correction(decision, feedback)

        elif feedback_type == 'preferential':
            # Update preference model
            self.preference_model.update(
                decision, feedback['preferred_alternative'], context
            )

        elif feedback_type == 'risk_adaptive':
            # Learn risk adaptation pattern
            self._learn_risk_adaptation(context, feedback)

        # Update human trust estimation
        trust_change = self._estimate_trust_change(feedback)
        return trust_change

Challenge 3: Real-Time Performance

My initial HADT implementation was computationally expensive, taking several seconds to generate responses—unacceptable for time-critical anomaly response.

Solution: Through experimentation with model distillation and specialized hardware acceleration, I achieved sub-second response times:

class OptimizedHADT(nn.Module):
    def __init__(self, teacher_model):
        super().__init__()
        # Knowledge distillation from full HADT
        self.student_model = self._distill_knowledge(teacher_model)

        # Quantization for faster inference
        self.quantized = quantize_dynamic(
            self.student_model,
            {nn.Linear, nn.MultiheadAttention},
            dtype=torch.qint8
        )

        # Precomputed constraint embeddings for common scenarios
        self.constraint_cache = ConstraintCache()

    def forward(self, state, action_space, constraint_ids):
        # Use cached constraint embeddings
        if constraint_ids in self.constraint_cache:
            constraints = self.constraint_cache[constraint_ids]
        else:
            constraints = self._encode_constraints(constraint_ids)

        # Optimized forward pass
        with torch.no_grad():
            output = self.quantized(state, action_space, constraints)

        return output

    @torch.compile(mode="reduce-overhead")
    def _encode_constraints(self, constraint_ids):
        # Compiled constraint encoding
        return self.constraint_encoder(constraint_ids)

Future Directions: Quantum-Enhanced Alignment

During my exploration of quantum computing for AI, I realized that quantum systems